Above are papers related to the following paper: [Paper_ID] Paper #25184. 	[Title] Feature Selection via Mathematical Programming [Abstract] The problem of discriminating between two finite point sets in n-dimensional feature space by a separating plane that utilizes as few of the features as possible, is formulated as a mathematical program with a parametric objective function and linear constraints. The step function that appears in the objective function can be approximated by a sigmoid or by a concave exponential on the nonnegative real line, or it can be treated exactly by considering the equivalent linear program with equilibrium constraints (LPEC). Computational tests of these three approaches on publicly available real-world databases have been carried out and compared with an adaptation of the optimal brain damage (OBD) method for reducing neural network complexity. One feature selection algorithm via concave minimization (FSV) reduced cross-validation error on a cancer prognosis database by 35.4% while reducing problem features from 32 to 4. Feature selection is an important problem in machine learning [18, 15, 16, 17, 33]. In its basic form the problem consists of eliminating as many of the features in a given problem as possible, while still carrying out a preassigned task with acceptable accuracy. Having a minimal number of features often leads to better generalization and simpler models that can be more easily interpreted. In the present work, our task is to discriminate between two given sets in an n-dimensional feature space by using as few of the given features as possible. We shall formulate this problem as a mathematical program with a parametric objective function that will attempt to achieve this task by generating a separating plane in a feature space of as small a dimension as possible while minimizing the average distance of misclassified points to the plane. One of the computational experiments that we carried out on our feature selection procedure showed its effectiveness, not only in minimizing the number of features selected, but also in quickly recognizing and removing spurious random features that were introduced. Thus, on the Wisconsin Prognosis Breast Cancer WPBC database [36] with a feature space of 32 dimensions and 6 random features added, one of our algorithms FSV (11) immediately removed the 6 random features as well as 28 of the original features resulting in a separating plane in a 4-dimensional reduced feature space. By using tenfold cross-validation [35], separation error in the 4-dimensional space was reduced 35.4% from the corresponding error in the original problem space. (See Section 3 for details.) We note that mathematical programming approaches to the feature selection problem have been recently proposed in [4, 22]. Even though the approach of [4] is based on an LPEC formulation, both the LPEC and its method of solution are different from the ones used here. The polyhedral concave minimization approach of [22] is principally involved with theoretical considerations of one specific algorithm and no cross-validatory results are given. Other effective computational applications of mathematical programming to neural networks are given in [30, 26]. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1125895. 	[Title] Packet Routing and Reinforcement Learning: Estimating Shortest Paths in Dynamic Graphs [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1125386. 	[Title] 21 Using n 2 classifier in constructive induction [Abstract] In this paper, we propose a multi-classification approach for constructive induction. The idea of an improvement of classification accuracy is based on iterative modification of input data space. This process is independently repeated for each pair of n classes. Finally, it gives (n 2 n)/2 input data subspaces of attributes dedicated for optimal discrimination of appropriate pairs of classes. We use genetic algorithms as a constructive induction engine. A final classification is obtained by a weighted majority voting rule, according to n 2 - classifier approach. The computational experiment was performed on medical data set. The obtained results point out the advantage of using a multi-classification model (n 2 classifier) in constructive induction in relation to the analogous single-classifier approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35343. 	[Title] Backpropagation Convergence Via Deterministic Nonmonotone Perturbed Minimization [Abstract] The fundamental backpropagation (BP) algorithm for training artificial neural networks is cast as a deterministic nonmonotone perturbed gradient method . Under certain natural assumptions, such as the series of learning rates diverging while the series of their squares converging, it is established that every accumulation point of the online BP iterates is a stationary point of the BP error function. The results presented cover serial and parallel online BP, modified BP with a momentum term, and BP with weight decay. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35863. 	[Title] Associative Reinforcement Learning: Functions in k-DNF [Abstract] An agent that must learn to act in the world by trial and error faces the reinforcement learning problem, which is quite different from standard concept learning. Although good algorithms exist for this problem in the general case, they are often quite inefficient and do not exhibit generalization. One strategy is to find restricted classes of action policies that can be learned more efficiently. This paper pursues that strategy by developing algorithms that can efficiently learn action maps that are expressible in k-DNF. The algorithms are compared with existing methods in empirical trials and are shown to have very good performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #144408. 	[Title] Neural net architectures for temporal sequence processing [Abstract] I present a general taxonomy of neural net architectures for processing time-varying patterns. This taxonomy subsumes many existing architectures in the literature, and points to several promising architectures that have yet to be examined. Any architecture that processes time-varying patterns requires two conceptually distinct components: a short-term memory that holds on to relevant past events and an associator that uses the short-term memory to classify or predict. My taxonomy is based on a characterization of short-term memory models along the dimensions of form, content, and adaptability. Experiments on predicting future values of a financial time series (US dollar-Swiss franc exchange rates) are presented using several alternative memory models. The results of these experiments serve as a baseline against which more sophisticated architectures can be compared. Neural networks have proven to be a promising alternative to traditional techniques for nonlinear temporal prediction tasks (e.g., Curtiss, Brandemuehl, & Kreider, 1992; Lapedes & Farber, 1987; Weigend, Huberman, & Rumelhart, 1992). However, temporal prediction is a particularly challenging problem because conventional neural net architectures and algorithms are not well suited for patterns that vary over time. The prototypical use of neural nets is in structural pattern recognition. In such a task, a collection of features|visual, semantic, or otherwise|is presented to a network and the network must categorize the input feature pattern as belonging to one or more classes. For example, a network might be trained to classify animal species based on a set of attributes describing living creatures such as "has tail", "lives in water", or "is carnivorous"; or a network could be trained to recognize visual patterns over a two-dimensional pixel array as a letter in fA; B; . . . ; Zg. In such tasks, the network is presented with all relevant information simultaneously. In contrast, temporal pattern recognition involves processing of patterns that evolve over time. The appropriate response at a particular point in time depends not only on the current input, but potentially all previous inputs. This is illustrated in Figure 1, which shows the basic framework for a temporal prediction problem. I assume that time is quantized into discrete steps, a sensible assumption because many time series of interest are intrinsically discrete, and continuous series can be sampled at a fixed interval. The input at time t is denoted x(t). For univariate series, this input Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #31769. 	[Title] Representing and Learning Visual Schemas in Neural Networks for Scene Analysis [Abstract] Using scene analysis as the task, this research focuses on three fundamental problems in neural network systems: (1) limited processing resources, (2) representing schemas, and (3) learning schemas. The first problem arises because no practical neural network can process all the visual input simultaneously and efficiently. The solution is to process a small amount of the input in parallel, and successively focus on the other parts of the input. This strategy requires that the system maintains structured knowledge for describing and interpreting the gathered information. The system should also learn to represent structured knowledge from examples of objects and scenes. VISOR, the system described in this paper, consists of three main components. The Low-Level Visual Module (simulated using procedural programs) extracts featural and positional information from the visual input. The Schema Module encodes structured knowledge about possible objects, and provides top-down information for the Low-Level Visual Module to focus attention at different parts of the scene. The Response Module learns to associate the schema activation patterns with external responses. It enables the external environment to provide reinforcement feedback for the learning of schematic structures. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #385572. 	[Title] Individual and Collective Prognostic Prediction [Abstract] The prediction of survival time or recurrence time is an important learning problem in medical domains. The Recurrence Surface Approximation (RSA) method is a natural, effective method for predicting recurrence times using censored input data. This paper introduces the Survival Curve RSA (SC-RSA), an extension to the RSA approach which produces accurate predicted rates of recurrence, while maintaining accuracy on individual predicted recurrence times. The method is applied to the problem of breast cancer recurrence using two different datasets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27174. 	[Title] Mathematical Programming in Neural Networks [Abstract] This paper highlights the role of mathematical programming, particularly linear programming, in training neural networks. A neural network description is given in terms of separating planes in the input space that suggests the use of linear programming for determining these planes. A more standard description in terms of a mean square error in the output space is also given, which leads to the use of unconstrained minimization techniques for training a neural network. The linear programming approach is demonstrated by a brief description of a system for breast cancer diagnosis that has been in use for the last four years at a major medical facility. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6184. 	[Title] Forward models: Supervised learning with a distal teacher [Abstract] Internal models of the environment have an important role to play in adaptive systems in general and are of particular importance for the supervised learning paradigm. In this paper we demonstrate that certain classical problems associated with the notion of the "teacher" in supervised learning can be solved by judicious use of learned internal models as components of the adaptive system. In particular, we show how supervised learning algorithms can be utilized in cases in which an unknown dynamical system intervenes between actions and desired outcomes. Our approach applies to any supervised learning algorithm that is capable of learning in multi-layer networks. *This paper is a revised version of MIT Center for Cognitive Science Occasional Paper #40. We wish to thank Michael Mozer, Andrew Barto, Robert Jacobs, Eric Loeb, and James McClelland for helpful comments on the manuscript. This project was supported in part by BRSG 2 S07 RR07047-23 awarded by the Biomedical Research Support Grant Program, Division of Research Resources, National Institutes of Health, by a grant from ATR Auditory and Visual Perception Research Laboratories, by a grant from Siemens Corporation, by a grant from the Human Frontier Science Program, and by grant N00014-90-J-1942 awarded by the Office of Naval Research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #259126. 	[Title] A Monotonic Measure for Optimal Feature Selection [Abstract] Feature selection is a problem of choosing a subset of relevant features. In general, only exhaustive search can bring about the optimal subset. With a monotonic measure, exhaustive search can be avoided without sacrificing optimality. Unfortunately, most error- or distance-based measures are not monotonic. A new measure is employed in this work that is monotonic and fast to compute. The search for relevant features according to this measure is guaranteed to be complete but not exhaustive. Experiments are conducted for verification. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1050679. 	[Title] Diplomarbeit A Genetic Algorithm for the Topological Optimization of Neural Networks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1140289. 	[Title] LU TP 93-24 Predicting System Loads with Artificial Neural [Abstract] Networks Methods and Results from Abstract: We devise a feed-forward Artificial Neural Network (ANN) procedure for predicting utility loads and present the resulting predictions for two test problems given by "The Great Energy Predictor Shootout The First Building Data Analysis and Prediction Competition" [1]. Key ingredients in our approach are a method (ffi -test) for determining relevant inputs and the Multilayer Perceptron. These methods are briefly reviewed together with comments on alternative schemes like fitting to polynomials and the use of recurrent networks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #948299. 	[Title] Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #16461. 	[Title] On the Applicability of Neural Network and Machine Learning Methodologies to Natural Language Processing [Abstract] We examine the inductive inference of a complex grammar specifically, we consider the task of training a model to classify natural language sentences as grammatical or ungrammatical, thereby exhibiting the same kind of discriminatory power provided by the Principles and Parameters linguistic framework, or Government-and-Binding theory. We investigate the following models: feed-forward neural networks, Fransconi-Gori-Soda and Back-Tsoi locally recurrent networks, Elman, Narendra & Parthasarathy, and Williams & Zipser recurrent networks, Euclidean and edit-distance nearest-neighbors, simulated annealing, and decision trees. The feed-forward neural networks and non-neural network machine learning models are included primarily for comparison. We address the question: How can a neural network, with its distributed nature and gradient descent based iterative calculations, possess linguistic capability which is traditionally handled with symbolic computation and recursive processes? Initial simulations with all models were only partially successful by using a large temporal window as input. Models trained in this fashion did not learn the grammar to a significant degree. Attempts at training recurrent networks with small temporal input windows failed until we implemented several techniques aimed at improving the convergence of the gradient descent training algorithms. We discuss the theory and present an empirical study of a variety of models and learning algorithms which highlights behaviour not present when attempting to learn a simpler grammar. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #96335. 	[Title] Geometry in Learning [Abstract] One of the fundamental problems in learning is identifying members of two different classes. For example, to diagnose cancer, one must learn to discriminate between benign and malignant tumors. Through examination of tumors with previously determined diagnosis, one learns some function for distinguishing the benign and malignant tumors. Then the acquired knowledge is used to diagnose new tumors. The perceptron is a simple biologically inspired model for this two-class learning problem. The perceptron is trained or constructed using examples from the two classes. Then the perceptron is used to classify new examples. We describe geometrically what a perceptron is capable of learning. Using duality, we develop a framework for investigating different methods of training a perceptron. Depending on how we define the "best" perceptron, different minimization problems are developed for training the perceptron. The effectiveness of these methods is evaluated empirically on four practical applications: breast cancer diagnosis, detection of heart disease, political voting habits, and sonar recognition. This paper does not assume prior knowledge of machine learning or pattern recognition. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35922. 	[Title] Mean Field Theory for Sigmoid Belief Networks [Abstract] We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. We demonstrate the utility of this framework on a benchmark problem in statistical pattern recognition|the classification of handwritten digits. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128536. 	[Title] MLC Tutorial A Machine Learning library of C classes. [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1103960. 	[Title] Surgery [Abstract] Object localization has applications in many areas of engineering and science. The goal is to spatially locate an arbitrarily-shaped object. In many applications, it is desirable to minimize the number of measurements collected for this purpose, while ensuring sufficient localization accuracy. In surgery, for example, collecting a large number of localization measurements may either extend the time required to perform a surgical procedure, or increase the radiation dosage to which a patient is exposed. Localization accuracy is a function of the spatial distribution of discrete measurements over an object when measurement noise is present. In [Simon et al., 1995a], metrics were presented to evaluate the information available from a set of discrete object measurements. In this study, new approaches to the discrete point data selection problem are described. These include hillclimbing, genetic algorithms (GAs), and Population-Based Incremental Learning (PBIL). Extensions of the standard GA and PBIL methods, which employ multiple parallel populations, are explored. The results of extensive empirical testing are provided. The results suggest that a combination of PBIL and hillclimbing result in the best overall performance. A computer-assisted surgical system which incorporates some of the methods presented in this paper is currently being evaluated in cadaver trials. Evolution-Based Methods for Selecting Point Data Shumeet Baluja was supported by a National Science Foundation Graduate Student Fellowship and a Graduate Student Fellowship from the National Aeronautics and Space Administration, administered by the Lyndon B. Johnson Space Center, Houston, TX. David Simon was partially supported by a National Science Foundation National Challenge grant (award IRI-9422734). for Object Localization: Applications to Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #25181. 	[Title] Bilinear Separation of Two Sets in n-Space [Abstract] The NP-complete problem of determining whether two disjoint point sets in the n-dimensional real space R n can be separated by two planes is cast as a bilinear program, that is minimizing the scalar product of two linear functions on a polyhedral set. The bilinear program, which has a vertex solution, is processed by an iterative linear programming algorithm that terminates in a finite number of steps at a point satisfying a necessary optimality condition or at a global minimum. Encouraging computational experience on a number of test problems is reported. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #30817. 	[Title] Analysis of Linsker's application of Hebbian rules to Linear Networks [Abstract] Linsker has reported the development of structured receptive fields in simulations using a Hebb-type synaptic plasticity rule in a feed-forward linear network. The synapses develop under dynamics determined by a matrix that is closely related to the covariance matrix of input cell activities. We analyse the dynamics of the learning rule in terms of the eigenvectors of this matrix. These eigenvectors represent independently evolving weight structures. Some general theorems are presented regarding the properties of these eigenvectors and their eigenvalues. For a general covariance matrix four principal parameter regimes are predicted. We concentrate on the gaussian covariances at layer B ! C of Linsker's network. Analytic and numerical solutions for the eigenvectors at this layer are presented. Three eigenvectors dominate the dynamics: a DC eigenvector, in which all synapses have the same sign; a bi-lobed, oriented eigenvector; and a circularly symmetric, centre-surround eigenvector. Analysis of the circumstances in which each of these vectors dominates yields an explanation of the emergence of centre-surround structures and symmetry-breaking bi-lobed structures. Criteria are developed estimating the boundary of the parameter regime in which centre-surround structures emerge. The application of our analysis to Linsker's higher layers, at which the covariance functions were oscillatory, is briefly discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #263279. 	[Title] Data Analyses Using Simulated Breeding and Inductive Learning Methods [Abstract] Marketing decision making tasks require the acquisition of efficient decision rules from noisy questionnaire data. Unlike popular learning-from-example methods, in such tasks, we must interpret the characteristics of the data without clear features of the data nor pre-determined evaluation criteria. The problem is how domain experts get simple, easy-to-understand, and accurate knowledge from noisy data. This paper describes a novel method to acquire efficient decision rules from questionnaire data using both simulated breeding and inductive learning techniques. The basic ideas of the method are that simulated breeding is used to get the effective features from the questionnaire data and that inductive learning is used to acquire simple decision rules from the data. The simulated breeding is one of the Genetic Algorithm based techniques to subjectively or interactively evaluate the qualities of offspring generated by genetic operations. The proposed method has been qualitatively and quantitatively validated by a case study on consumer product questionnaire data: the acquired rules are simpler than the results from the direct application of inductive learning; a domain expert admits that they are easy to understand; and they are at the same level on the accuracy compared with the other methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1154169. 	[Title]  [Abstract] 11] M.H. Overmars. A random approach to motion planning. Technical Report RUU-CS-92-32, Department of Computer Science, Utrecht University, October 1992. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107067. 	[Title] On the Greediness of Feature Selection Algorithms [Abstract] Based on our analysis and experiments using real-world datasets, we find that the greediness of forward feature selection algorithms does not severely corrupt the accuracy of function approximation using the selected input features, but improves the efficiency significantly. Hence, we propose three greedier algorithms in order to further enhance the efficiency of the feature selection processing. We provide empirical results for linear regression, locally weighted regression and k-nearest-neighbor models. We also propose to use these algorithms to develop an offline Chinese and Japanese handwriting recognition system with auto matically configured, local models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #396412. 	[Title] Boltzmann Machine learning using mean field theory and linear response correction [Abstract] We present a new approximate learning algorithm for Boltzmann Machines, using a systematic expansion of the Gibbs free energy to second order in the weights. The linear response correction to the correlations is given by the Hessian of the Gibbs free energy. The computational complexity of the algorithm is cubic in the number of neurons. We compare the performance of the exact BM learning algorithm with first order (Weiss) mean field theory and second order (TAP) mean field theory. The learning task consists of a fully connected Ising spin glass model on 10 neurons. We conclude that 1) the method works well for paramagnetic problems 2) the TAP correction gives a significant improvement over the Weiss mean field theory, both for paramagnetic and spin glass problems and 3) that the inclusion of diagonal weights improves the Weiss approximation for paramagnetic problems, but not for spin glass problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131647. 	[Title] Connectionist Modeling of the Fast Mapping Phenomenon [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #33412. 	[Title] Learning to Predict Reading Frames in E. coli DNA Sequences [Abstract] Two fundamental problems in analyzing DNA sequences are (1) locating the regions of a DNA sequence that encode proteins, and (2) determining the reading frame for each region. We investigate using artificial neural networks (ANNs) to find coding regions, determine reading frames, and detect frameshift errors in E. coli DNA sequences. We describe our adaptation of the approach used by Uberbacher and Mural to identify coding regions in human DNA, and we compare the performance of ANNs to several conventional methods for predicting reading frames. Our experiments demonstrate that ANNs can outperform these conventional approaches. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106568. 	[Title] Local Feedforward Networks [Abstract] Although feedforward neural networks are well suited to function approximation, in some applications networks experience problems when learning a desired function. One problem is interference which occurs when learning in one area of the input space causes unlearning in another area. Networks that are less susceptible to interference are referred to as spatially local networks. To understand these properties, a theoretical framework, consisting of a measure of interference and a measure of network localization, is developed that incorporates not only the network weights and architecture but also the learning algorithm. Using this framework to analyze sigmoidal multi-layer perceptron (MLP) networks that employ the back-prop learning algorithm, we address a familiar misconception that sigmoidal networks are inherently non-local by demonstrating that given a sufficiently large number of adjustable parameters, sigmoidal MLPs can be made arbitrarily local while retaining the ability to represent any continuous function on a compact domain. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #189577. 	[Title] Chain graphs for learning [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153169. 	[Title] Automated model selection [Abstract] Many algorithms have parameters that should be set by the user. For most machine learning algorithms parameter setting is a non-trivial task that influence knowledge model returned by the algorithm. Parameter values are usually set approximately according to some characteristics of the target problem, obtained in different ways. The usual way is to use background knowledge about the target problem (if any) and perform some testing experiments. The paper presents an approach to automated model selection based on local optimization that uses an empirical evaluation of the constructed concept description to guide the search. The approach was tested by using the inductive concept learning system Magnus Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1126037. 	[Title] Parallel Environments for Implementing Neural Networks [Abstract] As artificial neural networks (ANNs) gain popularity in a variety of application domains, it is critical that these models run fast and generate results in real time. Although a number of implementations of neural networks are available on sequential machines, most of these implementations require an inordinate amount of time to train or run ANNs, especially when the ANN models are large. One approach for speeding up the implementation of ANNs is to implement them on parallel machines. This paper surveys the area of parallel environments for the implementations of ANNs, and prescribes desired characteristics to look for in such implementations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3229. 	[Title] Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2 [Abstract] Neural computation, also called connectionism, parallel distributed processing, neural network modeling or brain-style computation, has grown rapidly in the last decade. Despite this explosion, and ultimately because of impressive applications, there has been a dire need for a concise introduction from a theoretical perspective, analyzing the strengths and weaknesses of connectionist approaches and establishing links to other disciplines, such as statistics or control theory. The Introduction to the Theory of Neural Computation by Hertz, Krogh and Palmer (subsequently referred to as HKP) is written from the perspective of physics, the home discipline of the authors. The book fulfills its mission as an introduction for neural network novices, provided that they have some background in calculus, linear algebra, and statistics. It covers a number of models that are often viewed as disjoint. Critical analyses and fruitful comparisons between these models Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3231. 	[Title] Irrelevant Features and the Subset Selection Problem [Abstract] We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features into useful categories of relevance. We present definitions for irrelevance and for two degrees of relevance. These definitions improve our understanding of the behavior of previous subset selection algorithms, and help define the subset of features that should be sought. The features selected should depend not only on the features and the target concept, but also on the induction algorithm. We describe a method for feature subset selection using cross-validation that is applicable to any induction algorithm, and discuss experiments conducted with ID3 and C4.5 on artificial and real datasets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152673. 	[Title] Misclassification Minimization [Abstract] The problem of minimizing the number of misclassified points by a plane, attempting to separate two point sets with intersecting convex hulls in n-dimensional real space, is formulated as a linear program with equilibrium constraints (LPEC). This general LPEC can be converted to an exact penalty problem with a quadratic objective and linear constraints. A Frank-Wolfe-type algorithm is proposed for the penalty problem that terminates at a stationary point or a global solution. Novel aspects of the approach include: (i) A linear complementarity formulation of the step function that "counts" misclassifications, (ii) Exact penalty formulation without boundedness, nondegeneracy or constraint qualification assumptions, (iii) An exact solution extraction from the sequence of minimizers of the penalty function for a finite value of the penalty parameter for the general LPEC and an explicitly exact solution for the LPEC with uncoupled constraints, and (iv) A parametric quadratic programming formulation of the LPEC associated with the misclassification minimization problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3237. 	[Title] Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology [Abstract] In the wrapper approach to feature subset selection, a search for an optimal set of features is made using the induction algorithm as a black box. The estimated future performance of the algorithm is the heuristic guiding the search. Statistical methods for feature subset selection including forward selection, backward elimination, and their stepwise variants can be viewed as simple hill-climbing techniques in the space of feature subsets. We utilize best-first search to find a good feature subset and discuss overfitting problems that may be associated with searching too many feature subsets. We introduce compound operators that dynamically change the topology of the search space to better utilize the information available from the evaluation of feature subsets. We show that compound operators unify previous approaches that deal with relevant and irrelevant features. The improved feature subset selection yields significant improvements for real-world datasets when using the ID3 and the Naive-Bayes induction algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #82087. 	[Title] Fast Probabilistic Modeling for Combinatorial Optimization [Abstract] Probabilistic models have recently been utilized for the optimization of large combinatorial search problems. However, complex probabilistic models that attempt to capture inter-parameter dependencies can have prohibitive computational costs. The algorithm presented in this paper, termed COMIT, provides a method for using probabilistic models in conjunction with fast search techniques. We show how COMIT can be used with two very different fast search algorithms: hillclimbing and Population-based incremental learning (PBIL). The resulting algorithms maintain many of the benefits of probabilistic modeling, with far less computational expense. Extensive empirical results are provided; COMIT has been successfully applied to jobshop scheduling, traveling salesman, and knapsack problems. This paper also presents a review of probabilistic modeling for combi natorial optimization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #180399. 	[Title] An Inductive Learning Approach to Prognostic Prediction [Abstract] This paper introduces the Recurrence Surface Approximation, an inductive learning method based on linear programming that predicts recurrence times using censored training examples, that is, examples in which the available training output may be only a lower bound on the "right answer." This approach is augmented with a feature selection method that chooses an appropriate feature set within the context of the linear programming generalizer. Computational results in the field of breast cancer prognosis are shown. A straightforward translation of the prediction method to an artificial neural network model is also proposed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #82098. 	[Title] Hill Climbing with Learning (An Abstraction of Genetic Algorithm) [Abstract] Simple modification of standard hill climbing optimization algorithm by taking into account learning features is discussed. Basic concept of this approach is the socalled probability vector, its single entries determine probabilities of appearance of '1' entries in n-bit vectors. This vector is used for the random generation of n-bit vectors that form a neighborhood (specified by the given probability vector). Within the neighborhood a few best solutions (with smallest functional values of a minimized function) are recorded. The feature of learning is introduced here so that the probability vector is updated by a formal analogue of Hebbian learning rule, well-known in the theory of artificial neural networks. The process is repeated until the probability vector entries are close either to zero or to one. The resulting probability vector unambiguously determines an n-bit vector which may be interpreted as an optimal solution of the given optimization task. Resemblance with genetic algorithms is discussed. Effectiveness of the proposed method is illustrated by an example of looking for global minima of a highly multimodal function. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #328370. 	[Title] Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle [Abstract] Reliable vision-based control of an autonomous vehicle requires the ability to focus attention on the important features in an input scene. Previous work with an autonomous lane following system, ALVINN [Pomerleau, 1993], has yielded good results in uncluttered conditions. This paper presents an artificial neural network based learning approach for handling difficult scenes which will confuse the ALVINN system. This work presents a mechanism for achieving task-specific focus of attention by exploiting temporal coherence. A saliency map, which is based upon a computed expectation of the contents of the inputs in the next time step, indicates which regions of the input retina are important for performing the task. The saliency map can be used to accentuate the features which are important for the task, and de-emphasize those which are not. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6334. 	[Title] Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms [Abstract] With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the term prototypes refers to the reference instances used in a nearest neighbor computation the instances with respect to which similarity is assessed in order to assign a class to a new data item. Both algorithms rely on stochastic techniques to search the space of sets of prototypes and are simple to implement. The first is a Monte Carlo sampling algorithm; the second applies random mutation hill climbing. On four datasets we show that only three or four prototypes sufficed to give predictive accuracy equal or superior to a basic nearest neighbor algorithm whose run-time storage costs were approximately 10 to 200 times greater. We briefly investigate how random mutation hill climbing may be applied to select features and prototypes simultaneously. Finally, we explain the performance of the sampling algorithm on these datasets in terms of a statistical measure of the extent of clustering displayed by the target classes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1112767. 	[Title] Alternative Discrete-Time Operators and Their Application to Nonlinear Models [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #636098. 	[Title] NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all [Abstract] In many learning problems, the learning system is presented with values for features that are actually irrelevant to the concept it is trying to learn. The FOCUS algorithm, due to Almuallim and Dietterich, performs an explicit search for the smallest possible input feature set S that permits a consistent mapping from the features in S to the output feature. The FOCUS algorithm can also be seen as an algorithm for learning determinations or functional dependencies, as suggested in [6]. Another algorithm for learning determinations appears in [7]. The FOCUS algorithm has superpolynomial runtime, but Almuallim and Di-etterich leave open the question of tractability of the underlying problem. In this paper, the problem is shown to be NP-complete. We also describe briefly some experiments that demonstrate the benefits of determination learning, and show that finding lowest-cardinality determinations is easier in practice than finding minimal determi Define the MIN-FEATURES problem as follows: given a set X of examples (which are each composed of a a binary value specifying the value of the target feature and a vector of binary values specifying the values of the other features) and a number n, determine whether or not there exists some feature set S such that: We show that MIN-FEATURES is NP-complete by reducing VERTEX-COVER to MIN-FEATURES. 1 The VERTEX-COVER problem may be stated as the question: given a graph G with vertices V and edges E, is there a subset V 0 of V , of size m, such that each edge in E is connected to at least one vertex in V 0 ? We may reduce an instance of VERTEX-COVER to an instance of MIN-FEATURES by mapping each edge in E to an example in X, with one input feature for every vertex in V . 1 In [8], a "proof" is reported for this result by reduction to set covering. The proof therefore fails to show NP-completeness. nations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1154251. 	[Title] Misclassification Minimization [Abstract] The problem of minimizing the number of misclassified points by a plane, attempting to separate two point sets with intersecting convex hulls in n-dimensional real space, is formulated as a linear program with equilibrium constraints (LPEC). This general LPEC can be converted to an exact penalty problem with a quadratic objective and linear constraints. A Frank-Wolfe-type algorithm is proposed for the penalty problem that terminates at a stationary point or a global solution. Novel aspects of the approach include: (i) A linear complementarity formulation of the step function that "counts" misclassifications, (ii) Exact penalty formulation without boundedness, nondegeneracy or constraint qualification assumptions, (iii) An exact solution extraction from the sequence of minimizers of the penalty function for a finite value of the penalty parameter for the general LPEC and an explicitly exact solution for the LPEC with uncoupled constraints, and (iv) A parametric quadratic programming formulation of the LPEC associated with the misclassification minimization problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39124. 	[Title] The Utility of Feature Weighting in Nearest-Neighbor Algorithms [Abstract] Nearest-neighbor algorithms are known to depend heavily on their distance metric. In this paper, we investigate the use of a weighted Euclidean metric in which the weight for each feature comes from a small set of options. We describe Diet, an algorithm that directs search through a space of discrete weights using cross-validation error as its evaluation function. Although a large set of possible weights can reduce the learner's bias, it can also lead to increased variance and overfitting. Our empirical study shows that, for many data sets, there is an advantage to weighting features, but that increasing the number of possible weights beyond two (zero and one) has very little benefit and sometimes degrades performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39126. 	[Title] Toward Optimal Feature Selection [Abstract] In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for defining the theoretically optimal, but computation-ally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature if it gives us little or no additional information beyond that subsumed by the remaining features. In particular, this will be the case for both irrelevant and redundant features. We then give an efficient algorithm for feature selection which computes an approximation to the optimal feature selection criterion. The conditions under which the approximate algorithm is successful are examined. Empirical results are given on a number of data sets, showing that the algorithm effectively han dles datasets with large numbers of features. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39127. 	[Title] Estimating Attributes: Analysis and Extensions of RELIEF [Abstract] In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was shown to be very efficient in estimating attributes. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are verified on various artificial and one well known real-world problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #157401. 	[Title] Convergence-Zone Episodic Memory: Analysis and Simulations [Abstract] Human episodic memory provides a seemingly unlimited storage for everyday experiences, and a retrieval system that allows us to access the experiences with partial activation of their components. The system is believed to consist of a fast, temporary storage in the hippocampus, and a slow, long-term storage within the neocortex. This paper presents a neural network model of the hippocampal episodic memory inspired by Damasio's idea of Convergence Zones. The model consists of a layer of perceptual feature maps and a binding layer. A perceptual feature pattern is coarse coded in the binding layer, and stored on the weights between layers. A partial activation of the stored features activates the binding pattern, which in turn reactivates the entire stored pattern. For many configurations of the model, a theoretical lower bound for the memory capacity can be derived, and it can be an order of magnitude or higher than the number of all units in the model, and several orders of magnitude higher than the number of binding-layer units. Computational simulations further indicate that the average capacity is an order of magnitude larger than the theoretical lower bound, and making the connectivity between layers sparser causes an even further increase in capacity. Simulations also show that if more descriptive binding patterns are used, the errors tend to be more plausible (patterns are confused with other similar patterns), with a slight cost in capacity. The convergence-zone episodic memory therefore accounts for the immediate storage and associative retrieval capability and large capacity of the hippocampal memory, and shows why the memory encoding areas can be much smaller than the perceptual maps, consist of rather coarse computational units, and be only sparsely connected to the perceptual maps. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1125082. 	[Title] Parallel Search for Neural Network  Under the guidance of [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39131. 	[Title] Oblivious Decision Trees and Abstract Cases [Abstract] In this paper, we address the problem of case-based learning in the presence of irrelevant features. We review previous work on attribute selection and present a new algorithm, Oblivion, that carries out greedy pruning of oblivious decision trees, which effectively store a set of abstract cases in memory. We hypothesize that this approach will efficiently identify relevant features even when they interact, as in parity concepts. We report experimental results on artificial domains that support this hypothesis, and experiments with natural domains that show improvement in some cases but not others. In closing, we discuss the implications of our experiments, consider additional work on irrelevant features, and outline some directions for future research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131748. 	[Title] Figure 1: The architecture of a Kohonen network. Each input neuron is fully connected with [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108209. 	[Title] Evolutionary Learning of the Crossover Operator [Abstract] 1 Abstract Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #167670. 	[Title] Error-Based and Entropy-Based Discretization of Continuous Features [Abstract] We present a comparison of error-based and entropy-based methods for discretization of continuous features. Our study includes both an extensive empirical comparison as well as an analysis of scenarios where error minimization may be an inappropriate discretization criterion. We present a discretization method based on the C4.5 decision tree algorithm and compare it to an existing entropy-based discretization algorithm, which employs the Minimum Description Length Principle, and a recently proposed error-based technique. We evaluate these discretization methods with respect to C4.5 and Naive-Bayesian classifiers on datasets from the UCI repository and analyze the computational complexity of each method. Our results indicate that the entropy-based MDL heuristic outperforms error minimization on average. We then analyze the shortcomings of error-based approaches in comparison to entropy-based methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #949511. 	[Title] Application of Neural Networks for the Classification of Diffuse Liver Disease by Quantitative Echography [Abstract] Three different methods were investigated to determine their ability to detect and classify various categories of diffuse liver disease. A statistical method, i.e., discriminant analysis, a supervised neural network called backpropagation and a nonsupervised, self-organizing feature map were examined. The investigation was performed on the basis of a previously selected set of acoustic and image texture parameters. The limited number of patients was successfully extended by generating additional but independent data with identical statistical properties. The generated data were used for training and test sets. The final test was made with the original patient data as a validation set. It is concluded that neural networks are an attractive alternative to traditional statistical techniques when dealing with medical detection and classification tasks. Moreover, the use of generated data for training the networks and the discriminant classifier has been shown to be justified and profitable. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #90888. 	[Title] GAL: Networks that grow when they learn and shrink when they forget [Abstract] Learning when limited to modification of some parameters has a limited scope; the capability to modify the system structure is also needed to get a wider range of the learnable. In the case of artificial neural networks, learning by iterative adjustment of synaptic weights can only succeed if the network designer predefines an appropriate network structure, i.e., number of hidden layers, units, and the size and shape of their receptive and projective fields. This paper advocates the view that the network structure should not, as usually done, be determined by trial-and-error but should be computed by the learning algorithm. Incremental learning algorithms can modify the network structure by addition and/or removal of units and/or links. A survey of current connectionist literature is given on this line of thought. "Grow and Learn" (GAL) is a new algorithm that learns an association at one-shot due to being incremental and using a local representation. During the so-called "sleep" phase, units that were previously stored but which are no longer necessary due to recent modifications are removed to minimize network complexity. The incrementally constructed network can later be finetuned off-line to improve performance. Another method proposed that greatly increases recognition accuracy is to train a number of networks and vote over their responses. The algorithm and its variants are tested on recognition of handwritten numerals and seem promising especially in terms of learning speed. This makes the algorithm attractive for on-line learning tasks, e.g., in robotics. The biological plausibility of incremental learning is also discussed briefly. Earlier part of this work was realized at the Laboratoire de Microinformatique of Ecole Polytechnique Federale de Lausanne and was supported by the Fonds National Suisse de la Recherche Scientifique. Later part was realized at and supported by the International Computer Science Institute. A number of people helped by guiding, stimulating discussions or questions: Subutai Ahmad, Peter Clarke, Jerry Feldman, Christian Jutten, Pierre Marchal, Jean Daniel Nicoud, Steve Omohondro and Leon Personnaz. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #390922. 	[Title] A MEAN FIELD LEARNING ALGORITHM FOR UNSUPERVISED NEURAL NETWORKS [Abstract] We introduce a learning algorithm for unsupervised neural networks based on ideas from statistical mechanics. The algorithm is derived from a mean field approximation for large, layered sigmoid belief networks. We show how to (approximately) infer the statistics of these networks without resort to sampling. This is done by solving the mean field equations, which relate the statistics of each unit to those of its Markov blanket. Using these statistics as target values, the weights in the network are adapted by a local delta rule. We evaluate the strengths and weaknesses of these networks for problems in statistical pattern recognition. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #447250. 	[Title] Introduction to Radial Basis Function Networks [Abstract] This document is an introduction to radial basis function (RBF) networks, a type of artificial neural network for application to problems of supervised learning (e.g. regression, classification and time series prediction). It is available in either PostScript or hyper-text 2 . Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152277. 	[Title] Misclassification Minimization [Abstract] The problem of minimizing the number of misclassified points by a plane, attempting to separate two point sets with intersecting convex hulls in n-dimensional real space, is formulated as a linear program with equilibrium constraints (LPEC). This general LPEC can be converted to an exact penalty problem with a quadratic objective and linear constraints. A Frank-Wolfe-type algorithm is proposed for the penalty problem that terminates at a stationary point or a global solution. Novel aspects of the approach include: (i) A linear complementarity formulation of the step function that "counts" misclassifications, (ii) Exact penalty formulation without boundedness, nondegeneracy or constraint qualification assumptions, (iii) An exact solution extraction from the sequence of minimizers of the penalty function for a finite value of the penalty parameter for the general LPEC and an explicitly exact solution for the LPEC with uncoupled constraints, and (iv) A parametric quadratic programming formulation of the LPEC associated with the misclassification minimization problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #118559. 	[Title] On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning [Abstract] Several researchers have demonstrated how neural networks can be trained to compensate for nonlinear signal distortion in e.g. digital satellite communications systems. These networks, however, require that both the original signal and its distorted version are known. Therefore, they have to be trained off-line, and they cannot adapt to changing channel characteristics. In this paper, a novel dual reinforcement learning approach is proposed that can adapt on-line while the system is performing. Assuming that the channel characteristics are the same in both directions, two predistorters at each end of the communication channel co-adapt using the output of the other predistorter to determine their own reinforcement. Using the common Volterra Series model to simulate the channel, the system is shown to successfully learn to compensate for distortions up to 30%, which is significantly higher than what might be expected in an actual channel. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #200480. 	[Title] CBET: a Case Base Exploration Tool [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #444191. 	[Title] Automatic Smoothing Spline Projection Pursuit  Automatic Smoothing Spline Projection Pursuit. [Abstract] kj = 1. The standard PPR algorithm of Friedman and Stuet-zle (1981) estimates the smooth functions f j using the supersmoother nonparametric scatterplot smoother. Friedman's algorithm constructs a model with M max linear combinations, then prunes back to a simpler model of size M M max , where M and M max are specified by the user. This paper discusses an alternative algorithm in which the smooth functions are estimated using smoothing splines. The direction coefficients ff j , the amount of smoothing in each direction, and the number of terms M and M max are determined to optimize a single generalized cross-validation measure. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #52515. 	[Title] Unsupervised learning of distributions on binary vectors using two layer networks [Abstract] We present a distribution model for binary vectors, called the influence combination model and show how this model can be used as the basis for unsupervised learning algorithms for feature selection. The model is closely related to the Harmonium model defined by Smolensky [RM86][Ch.6]. In the first part of the paper we analyze properties of this distribution representation scheme. We show that arbitrary distributions of binary vectors can be approximated by the combination model. We show how the weight vectors in the model can be interpreted as high order correlation patterns among the input bits. We compare the combination model with the mixture model and with principle component analysis. In the second part of the paper we present two algorithms for learning the combination model from examples. The first algorithm is based on gradient ascent. Here we give a closed form for this gradient that is significantly easier to compute than the corresponding gradient for the general Boltzmann machine. The second learning algorithm is a greedy method that creates the hidden units and computes their weights one at a time. This method is a variant of projection pursuit density estimation. In the third part of the paper we give experimental results for these learning methods on synthetic data and on natural data of handwritten digit images. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #29492. 	[Title] Induction of Multiscale Temporal Structure [Abstract] Learning structure in temporally-extended sequences is a difficult computational problem because only a fraction of the relevant information is available at any instant. Although variants of back propagation can in principle be used to find structure in sequences, in practice they are not sufficiently powerful to discover arbitrary contingencies, especially those spanning long temporal intervals or involving high order statistics. For example, in designing a connectionist network for music composition, we have encountered the problem that the net is able to learn musical structure that occurs locally in time|e.g., relations among notes within a musical phrase|but not structure that occurs over longer time periods|e.g., relations among phrases. To address this problem, we require a means of constructing a reduced description of the sequence that makes global aspects more explicit or more readily detectable. I propose to achieve this using hidden units that operate with different time constants. Simulation experiments indicate that slower time-scale hidden units are able to pick up global structure, structure that simply can not be learned by standard Many patterns in the world are intrinsically temporal, e.g., speech, music, the unfolding of events. Recurrent neural net architectures have been devised to accommodate time-varying sequences. For example, the architecture shown in Figure 1 can map a sequence of inputs to a sequence of outputs. Learning structure in temporally-extended sequences is a difficult computational problem because the input pattern may not contain all the task-relevant information at any instant. Thus, back propagation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105718. 	[Title] Computational Models of Sensorimotor Integration  Computational Maps and Motor Control. [Abstract] The sensorimotor integration system can be viewed as an observer attempting to estimate its own state and the state of the environment by integrating multiple sources of information. We describe a computational framework capturing this notion, and some specific models of integration and adaptation that result from it. Psychophysical results from two sensorimotor systems, subserving the integration and adaptation of visuo-auditory maps, and estimation of the state of the hand during arm movements, are presented and analyzed within this framework. These results suggest that: (1) Spatial information from visual and auditory systems is integrated so as to reduce the variance in localization. (2) The effects of a remapping in the relation between visual and auditory space can be predicted from a simple learning rule. (3) The temporal propagation of errors in estimating the hand's state is captured by a linear dynamic observer, providing evidence for the existence of an internal model which simulates the dynamic behavior of the arm. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113926. 	[Title] Improving RBF Networks by the Feature Selection Approach EUBAFES [Abstract] The curse of dimensionality is one of the severest problems concerning the application of RBF networks. The number of RBF nodes and therefore the number of training examples needed grows exponentially with the intrinsic dimensionality of the input space. One way to address this problem is the application of feature selection as a data preprocessing step. In this paper we propose a two-step approach for the determination of an optimal feature subset: First, all possible feature-subsets are reduced to those with best discrimination properties by the application of the fast and robust filter technique EUBAFES. Secondly we use a wrapper approach to judge, which of the pre-selected feature subsets leads to RBF networks with least complexity and best classification accuracy. Experiments are undertaken to show the improvement for RBF networks by our feature selection approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #49482. 	[Title] Topography And Ocular Dominance: A Model Exploring Positive Correlations [Abstract] The map from eye to brain in vertebrates is topographic, i.e. neighbouring points in the eye map to neighbouring points in the brain. In addition, when two eyes innervate the same target structure, the two sets of fibres segregate to form ocular dominance stripes. Experimental evidence from the frog and goldfish suggests that these two phenomena may be subserved by the same mechanisms. We present a computational model that addresses the formation of both topography and ocular dominance. The model is based on a form of competitive learning with subtractive enforcement of a weight normalization rule. Inputs to the model are distributed patterns of activity presented simultaneously in both eyes. An important aspect of this model is that ocular dominance segregation can occur when the two eyes are positively correlated, whereas previous models have tended to assume zero or negative correlations between the eyes. This allows investigation of the dependence of the pattern of stripes on the degree of correlation between the eyes: we find that increasing correlation leads to narrower stripes. Experiments are suggested to test this prediction. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #919885. 	[Title] Product Unit Learning constructive algorithm is then introduced which adds product units to a network [Abstract] Product units provide a method of automatically learning the higher-order input combinations required for the efficient synthesis of Boolean logic functions by neural networks. Product units also have a higher information capacity than sigmoidal networks. However, this activation function has not received much attention in the literature. A possible reason for this is that one encounters some problems when using standard backpropagation to train networks containing these units. This report examines these problems, and evaluates the performance of three training algorithms on networks of this type. Empirical results indicate that the error surface of networks containing product units have more local minima than corresponding networks with summation units. For this reason, a combination of local and global training algorithms were found to provide the most reliable convergence. We then investigate how `hints' can be added to the training algorithm. By extracting a common frequency from the input weights, and training this frequency separately, we show that convergence can be accelerated. In order to compare their performance with other transfer functions, product units were implemented as candidate units in the Cascade Correlation (CC) [13] system. Using these candidate units resulted in smaller networks which trained faster than when the any of the standard (three sigmoidal types and one Gaussian) transfer functions were used. This superiority was confirmed when a pool of candidate units of four different nonlinear activation functions were used, which have to compete for addition to the network. Extensive simulations showed that for the problem of implementing random Boolean logic functions, product units are always chosen above any of the other transfer functions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1115471. 	[Title] Discovering Compressive Partial Determinations in Mixed Numerical and Symbolic Domains [Abstract] Partial determinations are an interesting form of dependency between attributes in a relation. They generalize functional dependencies by allowing exceptions. We modify a known MDL formula for evaluating such partial determinations to allow for its use in an admissible heuristic in exhaustive search. Furthermore we describe an efficient preprocessing-based approach for handling numerical attributes. An empirical investigation tries to evaluate the viability of the presented ideas. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46431. 	[Title] Growing Simpler Decision Trees to Facilitate Knowledge Discovery [Abstract] When using machine learning techniques for knowledge discovery, output that is comprehensible to a human is as important as predictive accuracy. We introduce a new algorithm, SET-Gen, that improves the comprehensibility of decision trees grown by standard C4.5 without reducing accuracy. It does this by using genetic search to select the set of input features C4.5 is allowed to use to build its tree. We test SET-Gen on a wide variety of real-world datasets and show that SET-Gen trees are significantly smaller and reference significantly fewer features than trees grown by C4.5 without using SET-Gen. Statistical significance tests show that the accuracies of SET-Gen's trees are either not distinguishable from or are more accurate than those of the original C4.5 trees on all ten datasets tested. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108834. 	[Title] ce: [39] <author> Yoda, M. </author> <year> (1994). </year> <title> Predicting the Tokyo stock market. </title> <editor> In Deboeck, G.J. (Ed.) </editor> <year> (1994). </year> <title> Trading on the Edge. </title> <address> New York: </address> <publisher> Wiley., </publisher> <pages> 66-79. </pages> <institution> VITA Graduate School Southern Illinois University Daniel Nikolaev Nikovski Date of Birth: </institution> <address> April 13, 1969 606 West College Street, Apt.4, Rm. 6, Carbondale, Illinois 62901 150 Hristo Botev Boulevard, Apt. </address> <month> 54, </month> <title> 4004 Plovdiv, Bulgaria Technical University - Sofia, Bulgaria Engineer of Computer Systems and Control Thesis Title: Adaptive Computation Techniques for Time Series Analysis Major Professor: </title> <journal> Dr. Mehdi Zargham </journal> [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #100197. 	[Title] Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference, [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #7022. 	[Title] Transfer in a Connectionist Model of the Acquisition of Morphology [Abstract] The morphological systems of natural languages are replete with examples of the same devices used for multiple purposes: (1) the same type of morphological process (for example, suffixation for both noun case and verb tense) and (2) identical morphemes (for example, the same suffix for English noun plural and possessive). These sorts of similarity would be expected to convey advantages on language learners in the form of transfer from one morphological category to another. Connectionist models of morphology acquisition have been faulted for their supposed inability to represent phonological similarity across morphological categories and hence to facilitate transfer. This paper describes a connectionist model of the acquisition of morphology which is shown to exhibit transfer of this type. The model treats the morphology acquisition problem as one of learning to map forms onto meanings and vice versa. As the network learns these mappings, it makes phonological generalizations which are embedded in connection weights. Since these weights are shared by different morphological categories, transfer is enabled. In a set of experiments with artificial stimuli, networks were trained first on one morphological task (e.g., tense) and then on a second (e.g., number). It is shown that in the context of suffixation, prefixation, and template rules, the second task is facilitated when the second category either makes use of the same forms or the same general process type (e.g., prefixation) as the first. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #83826. 	[Title] Beyond the Cognitive Map: Contributions to a Computational Neuroscience Theory of Rodent Navigation for the [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20850. 	[Title] Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise [Abstract] Irrelevant and redundant features may reduce both predictive accuracy and comprehensibility of induced concepts. Most common Machine Learning approaches for selecting a good subset of relevant features rely on cross-validation. As an alternative, we present the application of a particular Minimum Description Length (MDL) measure to the task of feature subset selection. Using the MDL principle allows taking into account all of the available data at once. The new measure is information-theoretically plausible and yet still simple and therefore efficiently computable. We show empirically that this new method for judging the value of feature subsets is more efficient than and performs at least as well as methods based on cross-validation. Domains with both a large number of training examples and a large number of possible features yield the biggest gains in efficiency. Thus our new approach seems to scale up better to large learning problems than previous methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1132418. 	[Title] NONPARAMETRIC SELECTION OF INPUT VARIABLES FOR CONNECTIONIST LEARNING [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17798. 	[Title] Using Decision Trees to Improve Case-Based Learning [Abstract] This paper shows that decision trees can be used to improve the performance of case-based learning (CBL) systems. We introduce a performance task for machine learning systems called semi-flexible prediction that lies between the classification task performed by decision tree algorithms and the flexible prediction task performed by conceptual clustering systems. In semi-flexible prediction, learning should improve prediction of a specific set of features known a priori rather than a single known feature (as in classification) or an arbitrary set of features (as in conceptual clustering). We describe one such task from natural language processing and present experiments that compare solutions to the problem using decision trees, CBL, and a hybrid approach that combines the two. In the hybrid approach, decision trees are used to specify the features to be included in k-nearest neighbor case retrieval. Results from the experiments show that the hybrid approach outperforms both the decision tree and case-based approaches as well as two case-based systems that incorporate expert knowledge into their case retrieval algorithms. Results clearly indicate that decision trees can be used to improve the performance of CBL systems and do so without reliance on potentially expensive expert knowledge. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128839. 	[Title] Requirements and use of neural networks for industrial appli- [Abstract] Modern industry of today needs flexible, adaptive and fault-tolerant methods for information processing. Several applications have shown that neural networks fulfill these requirements. In this paper application areas, in which neural networks have been successfully used, are presented. Then a kind of check list is described, which mentioned the different steps, when applying neural networks. The paper finished with a discussion of some neural networks projects done in the research group Interactive Planning at the Research Center for Computer Science (FZI). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #104840. 	[Title] An Optimized Theory Revision Module [Abstract] Theory revision systems typically use a set of theory-to-theory transformations f k g to hill-climb from a given initial theory to a new theory whose empirical accuracy, over a given set of labeled training instances fc j g, is a local maximum. At the heart of each such process is an "evaluator", which compares the accuracy of the current theory KB with that of each of its "neighbors" f k (KB)g, with the goal of determining which neighbor has the highest accuracy. The obvious "wrapper" evaluator simply evaluates each individual neighbor theory KB k = k (KB) on each instance c j . As it can be very expensive to evaluate a single theory on a single instance, and there can be a great many training instances and a huge number of neighbors, this approach can be prohibitively slow. We present an alternative system which employs a smarter evaluator that quickly computes the accuracy of a transformed theory k (KB) by "looking inside" KB and reasoning about the effects of the k transformation. We compare the performance of with the naive wrapper system on real-world theories obtained from a fielded expert system, and find that runs over 35 times faster than , while attaining the same accuracy. This paper also discusses 's source of power. Keywords: theory revision, efficient algorithm, hill-climbing system Multiple Submissions: We have submited a related version of this paper to AAAI96. fl We gratefully acknowledge the many helpful comments on this report from George Drastal, Chandra Mouleeswaran and Geoff Towell. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #62347. 	[Title] Embodiment and Manipulation Learning Process for a Humanoid Hand [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1109392. 	[Title] Disambiguation and Grammar as Emergent Soft Constraints [Abstract] When reading a sentence such as "The diplomat threw the ball in the ballpark for the princess" our interpretation changes from a dance event to baseball and back to dance. Such on-line disambiguation happens automatically and appears to be based on dynamically combining the strengths of association between the keywords and the two senses. Subsymbolic neural networks are very good at modeling such behavior. They learn word meanings as soft constraints on interpretation, and dynamically combine these constraints to form the most likely interpretation. On the other hand, it is very difficult to show how systematic language structures such as relative clauses could be processed in such a system. The network would only learn to associate them to specific contexts and would not be able to process new combinations of them. A closer look at understanding embedded clauses shows that humans are not very systematic in processing grammatical structures either. For example, "The girl who the boy who the girl who lived next door blamed hit cried" is very difficult to understand, whereas "The car that the man who the dog that had rabies bit drives is in the garage" is not. This difference emerges from the same semantic constraints that are at work in the disambiguation task. In this chapter we will show how the subsymbolic parser can be combined with high-level control that allows the system to process novel combinations of relative clauses systematically, while still being sensitive to the semantic constraints. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106330. 	[Title] Induction of Condensed Determinations [Abstract] In this paper we suggest determinations as a representation of knowledge that should be easy to understand. We briefly review determinations, which can be displayed in a tabular format, and their use in prediction, which involves a simple matching process. We describe ConDet, an algorithm that uses feature selection to construct determinations from training data, augmented by a condensation process that collapses rows to produce simpler structures. We report experiments that show condensation reduces complexity with no loss of accuracy, then discuss ConDet's relation to other work and outline directions for future studies. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #92065. 	[Title] ADAPTIVE REGULARIZATION [Abstract] Regularization, e.g., in the form of weight decay, is important for training and optimization of neural network architectures. In this work we provide a tool based on asymptotic sampling theory, for iterative estimation of weight decay parameters. The basic idea is to do a gradient descent in the estimated generalization error with respect to the regularization parameters. The scheme is implemented in our Designer Net framework for network training and pruning, i.e., is based on the diagonal Hessian approximation. The scheme does not require essential computational overhead in addition to what is needed for training and pruning. The viability of the approach is demonstrated in an experiment concerning prediction of the chaotic Mackey-Glass series. We find that the optimized weight decays are relatively large for densely connected networks in the initial pruning phase, while they decrease as pruning proceeds. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1130915. 	[Title] Combining Neural Network Forecasts on Wavelet-Transformed Time Series [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128868. 	[Title] Identification of Protein Coding Regions In Genomic DNA Molecular, Cellular and Developmental Biology, Keywords: gene [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #72101. 	[Title] Constructive Learning of Recurrent Neural Networks: Limitations of Recurrent Casade Correlation and a Simple Solution [Abstract] It is often difficult to predict the optimal neural network size for a particular application. Constructive or destructive methods that add or subtract neurons, layers, connections, etc. might offer a solution to this problem. We prove that one method, Recurrent Cascade Correlation, due to its topology, has fundamental limitations in representation and thus in its learning capabilities. It cannot represent with monotone (i.e. sigmoid) and hard-threshold activation functions certain finite state automata. We give a "preliminary" approach on how to get around these limitations by devising a simple constructive training method that adds neurons during training while still preserving the powerful fully-recurrent structure. We illustrate this approach by simulations which learn many examples of regular grammars that the Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #48550. 	[Title] Identification and Control of Nonlinear Systems Using Neural Network Models: Design and Stability Analysis [Abstract] Report 91-09-01 September 1991 (revised) May 1994 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102761. 	[Title] Knowledge Discovery in International Conflict Databases [Abstract] Artificial Intelligence is heavily supported by military institutions, while practically no effort goes into the investigation of possible contributions of AI to the avoidance and termination of crises and wars. This paper makes a first step into this direction by investigating the use of machine learning techniques for discovering knowledge in international conflict and conflict management databases. We have applied similarity-based case retrieval to the KOSIMO database of international conflicts. Furthermore, we present results of analyzing the CONFMAN database of successful and unsuccessful conflict management attempts with an inductive decision tree learning algorithm. The latter approach seems to be particularly promising, as conflict management events apparently are more repetitive and thus better suited for machine-aided analysis. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #133550. 	[Title] OBSERVABILITY IN RECURRENT NEURAL NETWORKS [Abstract] Report SYCON-92-07rev ABSTRACT We obtain a characterization of observability for a class of nonlinear systems which appear in neural networks research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1130927. 	[Title] Parametric regression 1.1 Learning problem model f bw b w in turn is an estimator [Abstract] Let us present briefly the learning problem we will address in this chapter and the following. The ultimate goal is the modelling of a mapping f : x 7! y from multidimensional input x to output y. The output can be multi-dimensional, but we will mostly address situations where it is a one dimensional real value. Furthermore, we should take into account the fact that we scarcely ever observe the actual true mapping y = f (x). This is due to perturbations such as e.g. observational noise. We will rather have a joint probability p (x; y). We expect this probability to be peaked for values of x and y corresponding to the mapping. We focus on automatic learning by example. A set D = of data sampled from the joint distribution p (x; y) = p (yjx) p (x) is collected. With the help of this set, we try to identify a model of the data, parameterised by a set of 1.2 Learning and optimisation The fit of the model to the system in a given point x is measured using a criterion representing the distance from the model prediction b y to the system, e (y; f w (x)). This is the local risk . The performance of the model is measured by the expected This quantity represents the ability to yield good performance for all the possible situations (i.e. (x; y) pairs) and is thus called generalisation error . The optimal set 1 parameters w: f w : x 7! b y. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1130931. 	[Title] TH presentee par STATISTICAL LEARNING AND REGULARISATION FOR REGRESSION Application to system identification and time [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #641976. 	[Title] A comparison of some error estimates for neural network models  Summary [Abstract] We discuss a number of methods for estimating the standard error of predicted values from a multi-layer perceptron. These methods include the delta method based on the Hessian, bootstrap estimators, and the "sandwich" estimator. The methods are described and compared in a number of examples. We find that the bootstrap methods perform best, partly because they capture variability due to the choice of starting weights. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10169. 	[Title] Learning Boolean Concepts in the Presence of Many Irrelevant Features [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106370. 	[Title] The Effective Size of a Neural Network: A Principal Component Approach [Abstract] Often when learning from data, one attaches a penalty term to a standard error term in an attempt to prefer simple models and prevent overfitting. Current penalty terms for neural networks, however, often do not take into account weight interaction. This is a critical drawback since the effective number of parameters in a network usually differs dramatically from the total number of possible parameters. In this paper, we present a penalty term that uses Principal Component Analysis to help detect functional redundancy in a neural network. Results show that our new algorithm gives a much more accurate estimate of network complexity than do standard approaches. As a result, our new term should be able to improve techniques that make use of a penalty term, such as weight decay, weight pruning, feature selection, Bayesian, and prediction-risk tech niques. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #23502. 	[Title] Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon [Abstract] DISLEX is an artificial neural network model of the mental lexicon. It was built to test com-putationally whether the lexicon could consist of separate feature maps for the different lexical modalities and the lexical semantics, connected with ordered pathways. In the model, the orthographic, phonological, and semantic feature maps and the associations between them are formed in an unsupervised process, based on cooccurrence of the lexical symbol and its meaning. After the model is organized, various damage to the lexical system can be simulated, resulting in dyslexic and category-specific aphasic impairments similar to those observed in human patients. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131471. 	[Title] Search-based Class Discretization [Abstract] We present a methodology that enables the use of classification algorithms on regression tasks. We implement this method in system RECLA that transforms a regression problem into a classification one and then uses an existent classification system to solve this new problem. The transformation consists of mapping a continuous variable into an ordinal variable by grouping its values into an appropriate set of intervals. We use misclassification costs as a means to reflect the implicit ordering among the ordinal values of the new variable. We describe a set of alternative discretization methods and, based on our experimental results, justify the need for a search-based approach to choose the best method. Our experimental results confirm the validity of our search-based approach to class discretization, and reveal the accuracy benefits of adding misclassification costs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #285675. 	[Title] Parsimonious Least Norm Approximation [Abstract] A theoretically justifiable fast finite successive linear approximation algorithm is proposed for obtaining a parsimonious solution to a corrupted linear system Ax = b + p, where the corruption p is due to noise or error in measurement. The proposed linear-programming-based algorithm finds a solution x by parametrically minimizing the number of nonzero elements in x and the error k Ax b p k 1 . Numerical tests on a signal-processing-based example indicate that the proposed method is comparable to a method that parametrically minimizes the 1-norm of the solution x and the error k Ax b p k 1 , and that both methods are superior, by orders of magnitude, to solutions obtained by least squares as well by combinatorially choosing an optimal solution with a specific number of nonzero elements. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27631. 	[Title] Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning [Abstract] This paper explores issues involved in implementing robot learning for a challenging dynamic task, using a case study from robot juggling. We use a memory-based local model - ing approach (locally weighted regression) to represent a learned model of the task to be performed. Statistical tests are given to examine the uncertainty of a model, to optimize its pre diction quality, and to deal with noisy and corrupted data. We develop an exploration algorithm that explicitly deals with prediction accuracy requirements dur ing explo - ration. Using all these ingredients in combination with methods from optimal control, our robot achieves fast real - time learning of the task within 40 to 100 trials. * Address of both authors: Massachusetts Institute of Technology, The Artificial Intelligence Laboratory & The Department of Brain and Cognitive Sciences, 545 Technology Square, Cambride, MA 02139, USA. Email: ss-chaal@ai.mit.edu, cga@ai.mit.edu. Support was provided by the Air Force Office of Sci entific Research and by Siemens Cor pora tion. Support for the first author was provided by the Ger man Scholar ship Foundation and the Alexander von Hum boldt Founda tion. Support for the second author was provided by a Na tional Sci ence Foundation Pre sidential Young Investigator Award. We thank Gideon Stein for im ple ment ing the first version of LWR on the i860 microprocessor, and Gerrie van Zyl for build ing the devil stick robot and implementing the first version of devil stick learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63477. 	[Title] Efficient Feature Selection in Conceptual Clustering [Abstract] Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We investigate the potential for similar benefits in an unsupervised learning task, conceptual clustering. The issues raised in feature selection by the absence of class labels are discussed and an implementation of a sequential feature selection algorithm based on an existing conceptual clustering system is described. Additionally, we present a second implementation which employs a technique for improving the efficiency of the search for an optimal description and compare the performance of both algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #654326. 	[Title] Evolutionary Training of CLP-Constrained Neural Networks [Abstract] The paper is concerned with the integration of constraint logic programming systems (CLP) with systems based on genetic algorithms (GA). The resulting framework is tailored for applications that require a first phase in which a number of constraints need to be generated, and a second phase in which an optimal solution satisfying these constraints is produced. The first phase is carried by the CLP and the second one by the GA. We present a specific framework where ECL i PS e (ECRC Common Logic Programming System) and GENOCOP (GEnetic algorithm for Numerical Optimization for COnstrained Problems) are integrated in a framework called CoCo (COmputational intelligence plus COnstraint logic programming). The CoCo system is applied to the training problem for neural networks. We consider constrained networks, e.g. neural networks with shared weights, constraints on the weights for example domain constraints for hardware implementation etc. Then ECL i PS e is used to generate the chromosome representation together with other constraints which ensure, in most cases, that each network is specified by exactly one chromosome. Thus the problem becomes a constrained optimization problem, where the optimization criterion is to optimize the error of the network, and GENOCOP is used to find an optimal solution. Note: The work of the second author was partially supported by SION, a department of the NWO, the National Foundation for Scientific Research. This work has been carried out while the third author was visiting CWI, Amsterdam, and the fourth author was visiting Leiden University. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #49660. 	[Title] Feature Subset Selection as Search with Probabilistic Estimates [Abstract] Irrelevant features and weakly relevant features may reduce the comprehensibility and accuracy of concepts induced by supervised learning algorithms. We formulate the search for a feature subset as an abstract search problem with probabilistic estimates. Searching a space using an evaluation function that is a random variable requires trading off accuracy of estimates for increased state exploration. We show how recent feature subset selection algorithms in the machine learning literature fit into this search problem as simple hill climbing approaches, and conduct a small experiment using a best-first search technique. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63486. 	[Title] Evaluation and Selection of Biases in Machine Learning [Abstract] In this introduction, we define the term bias as it is used in machine learning systems. We motivate the importance of automated methods for evaluating and selecting biases using a framework of bias selection as search in bias and meta-bias spaces. Recent research in the field of machine learning bias is summarized. Please summarise all the given information.

Paper #25184 has the title `Feature Selection via Mathematical Programming  
` and the abstract `The problem of discriminating between two finite point sets in n-dimensional feature space by a separating plane that utilizes as few of the features as possible, is formulated as a mathematical program with a parametric objective function and linear constraints. The step function that appears in the objective function can be approximated by a sigmoid or by a concave exponential on the nonnegative real line, or it can be treated exactly by considering the equivalent linear program with equilibrium constraints (LPEC). Computational tests of these three approaches on publicly available real-world databases have been carried out and compared with an adaptation of the optimal brain damage (OBD) method for reducing neural network complexity. One feature selection algorithm via concave minimization (FSV) reduced cross-validation error on a cancer prognosis database by 35.4% while reducing problem features from 32 to 4. Feature selection is an important problem in machine learning [18, 15, 16, 17, 33]. In its basic form the problem consists of eliminating as many of the features in a given problem as possible, while still carrying out a preassigned task with acceptable accuracy. Having a minimal number of features often leads to better generalization and simpler models that can be more easily interpreted. In the present work, our task is to discriminate between two given sets in an n-dimensional feature space by using as few of the given features as possible. We shall formulate this problem as a mathematical program with a parametric objective function that will attempt to achieve this task by generating a separating plane in a feature space of as small a dimension as possible while minimizing the average distance of misclassified points to the plane. One of the computational experiments that we carried out on our feature selection procedure showed its effectiveness, not only in minimizing the number of features selected, but also in quickly recognizing and removing spurious random features that were introduced. Thus, on the Wisconsin Prognosis Breast Cancer WPBC database [36] with a feature space of 32 dimensions and 6 random features added, one of our algorithms FSV (11) immediately removed the 6 random features as well as 28 of the original features resulting in a separating plane in a 4-dimensional reduced feature space. By using tenfold cross-validation [35], separation error in the 4-dimensional space was reduced 35.4% from the corresponding error in the original problem space. (See Section 3 for details.) We note that mathematical programming approaches to the feature selection problem have been recently proposed in [4, 22]. Even though the approach of [4] is based on an LPEC formulation, both the LPEC and its method of solution are different from the ones used here. The polyhedral concave minimization approach of [22] is principally involved with theoretical considerations of one specific algorithm and no cross-validatory results are given. Other effective computational applications of mathematical programming to neural networks are given in [30, 26]. 
`.
Question: Which category should Paper #25184 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #25184 has the title `Feature Selection via Mathematical Programming  
` and the abstract `The problem of discriminating between two finite point sets in n-dimensional feature space by a separating plane that utilizes as few of the features as possible, is formulated as a mathematical program with a parametric objective function and linear constraints. The step function that appears in the objective function can be approximated by a sigmoid or by a concave exponential on the nonnegative real line, or it can be treated exactly by considering the equivalent linear program with equilibrium constraints (LPEC). Computational tests of these three approaches on publicly available real-world databases have been carried out and compared with an adaptation of the optimal brain damage (OBD) method for reducing neural network complexity. One feature selection algorithm via concave minimization (FSV) reduced cross-validation error on a cancer prognosis database by 35.4% while reducing problem features from 32 to 4. Feature selection is an important problem in machine learning [18, 15, 16, 17, 33]. In its basic form the problem consists of eliminating as many of the features in a given problem as possible, while still carrying out a preassigned task with acceptable accuracy. Having a minimal number of features often leads to better generalization and simpler models that can be more easily interpreted. In the present work, our task is to discriminate between two given sets in an n-dimensional feature space by using as few of the given features as possible. We shall formulate this problem as a mathematical program with a parametric objective function that will attempt to achieve this task by generating a separating plane in a feature space of as small a dimension as possible while minimizing the average distance of misclassified points to the plane. One of the computational experiments that we carried out on our feature selection procedure showed its effectiveness, not only in minimizing the number of features selected, but also in quickly recognizing and removing spurious random features that were introduced. Thus, on the Wisconsin Prognosis Breast Cancer WPBC database [36] with a feature space of 32 dimensions and 6 random features added, one of our algorithms FSV (11) immediately removed the 6 random features as well as 28 of the original features resulting in a separating plane in a 4-dimensional reduced feature space. By using tenfold cross-validation [35], separation error in the 4-dimensional space was reduced 35.4% from the corresponding error in the original problem space. (See Section 3 for details.) We note that mathematical programming approaches to the feature selection problem have been recently proposed in [4, 22]. Even though the approach of [4] is based on an LPEC formulation, both the LPEC and its method of solution are different from the ones used here. The polyhedral concave minimization approach of [22] is principally involved with theoretical considerations of one specific algorithm and no cross-validatory results are given. Other effective computational applications of mathematical programming to neural networks are given in [30, 26]. 
`.
Question: Which category should Paper #25184 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks