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 #1135108. 	[Title] A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian [Abstract] We describe the maximum-likelihood parameter estimation problem and how the Expectation-Maximization (EM) algorithm can be used for its solution. We first describe the abstract form of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) finding the parameters of a hidden Markov model (HMM) (i.e., the Baum-Welch algorithm) for both discrete and Gaussian mixture observation models. We derive the update equations in fairly explicit detail but we do not prove any convergence properties. We try to emphasize intuition rather than mathematical rigor. 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 #66594. 	[Title] Automatic Parameter Selection by Minimizing Estimated Error [Abstract] We address the problem of finding the parameter settings that will result in optimal performance of a given learning algorithm using a particular dataset as training data. We describe a "wrapper" method, considering determination of the best parameters as a discrete function optimization problem. The method uses best-first search and cross-validation to wrap around the basic induction algorithm: the search explores the space of parameter values, running the basic algorithm many times on training and holdout sets produced by cross-validation to get an estimate of the expected error of each parameter setting. Thus, the final selected parameter settings are tuned for the specific induction algorithm and dataset being studied. We report experiments with this method on 33 datasets selected from the UCI and StatLog collections using C4.5 as the basic induction algorithm. At a 90% confidence level, our method improves the performance of C4.5 on nine domains, degrades performance on one, and is statistically indistinguishable from C4.5 on the rest. On the sample of datasets used for comparison, our method yields an average 13% relative decrease in error rate. We expect to see similar performance improvements when using our method with other machine learning al gorithms. 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 #1104449. 	[Title] Using Neural Networks for Descriptive Statistical Analysis of Educational Data [Abstract] In this paper we discuss the methodological issues of using a class of neural networks called Mixture Density Networks (MDN) for discriminant analysis. MDN models have the advantage of having a rigorous probabilistic interpretation, and they have proven to be a viable alternative as a classification procedure in discrete domains. We will address both the classification and interpretive aspects of discriminant analysis, and compare the approach to the traditional method of linear discrimin- ants as implemented in standard statistical packages. We show that the MDN approach adopted performs well in both aspects. Many of the observations made are not restricted to the particular case at hand, and are applicable to most applications of discriminant analysis in educational research. fl URL: http://www.cs.Helsinki.FI/research/cosco/ Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #428610. 	[Title] Non-linear Models for Time Series Using Mixtures of Experts [Abstract] We consider a novel non-linear model for time series analysis. The study of this model emphasizes both theoretical aspects as well as practical applicability. The architecture of the model is demonstrated to be sufficiently rich, in the sense of approximating unknown functional forms, yet it retains some of the simple and intuitive characteristics of linear models. A comparison to some more established non-linear models will be emphasized, and theoretical issues are backed by prediction results for benchmark time series, as well as computer generated data sets. Efficient estimation algorithms are seen to be applicable, made possible by the mixture based structure of the model. Large sample properties of the estimators are discussed as well, in both well specified as well as misspecified settings. We also demonstrate how inference pertaining to the data structure may be made from the parameterization of the model, resulting in a better, more intuitive, understanding of the structure and performance of the model. 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 #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 #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 #753265. 	[Title] Validation of Average Error Rate Over Classifiers [Abstract] We examine methods to estimate the average and variance of test error rates over a set of classifiers. We begin with the process of drawing a classifier at random for each example. Given validation data, the average test error rate can be estimated as if validating a single classifier. Given the test example inputs, the variance can be computed exactly. Next, we consider the process of drawing a classifier at random and using it on all examples. Once again, the expected test error rate can be validated as if validating a single classifier. However, the variance must be estimated by validating all classifers, which yields loose or uncertain bounds. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8821. 	[Title] Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome [Abstract] This paper tries to identify rules and factors that are predictive for the outcome of international conflict management attempts. We use C4.5, an advanced Machine Learning algorithm, for generating decision trees and prediction rules from cases in the CONFMAN database. The results show that simple patterns and rules are often not only more understandable, but also more reliable than complex rules. Simple decision trees are able to improve the chances of correctly predicting the outcome of a conflict management attempt. This suggests that mediation is more repetitive than conflicts per se, where such results have not been achieved so far. 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 #37884. 	[Title] Hidden Markov decision trees [Abstract] We study a time series model that can be viewed as a decision tree with Markov temporal structure. The model is intractable for exact calculations, thus we utilize variational approximations. We consider three different distributions for the approximation: one in which the Markov calculations are performed exactly and the layers of the decision tree are decoupled, one in which the decision tree calculations are performed exactly and the time steps of the Markov chain are decoupled, and one in which a Viterbi-like assumption is made to pick out a single most likely state sequence. We present simulation results for artificial data and the Bach chorales. Accepted for oral presentation at NIPS*96. 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 #12946. 	[Title] Learning from incomplete data [Abstract] Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives|the likelihood-based and the Bayesian. The goal is two-fold: to place current neural network approaches to missing data within a statistical framework, and to describe a set of algorithms, derived from the likelihood-based framework, that handle clustering, classification, and function approximation from incomplete data in a principled and efficient manner. These algorithms are based on mixture modeling and make two distinct appeals to the Expectation-Maximization (EM) principle (Dempster et al., 1977)|both for the estimation of mixture components and for coping with the missing data. This report describes research done at the Center for Biological and Computational Learning and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Center is provided in part by a grant from the National Science Foundation under contract ASC-9217041. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense. The authors were supported in part by a grant from ATR Auditory and Visual Perception Research Laboratories, by a grant from Siemens Corporation, by grant IRI-9013991 from the National Science Foundation, and by grant N00014-90-J-1942 from the Office of Naval Research. Zoubin Ghahramani was supported by a grant from the McDonnell-Pew Foundation. Michael I. Jordan is a NSF Presidential Young Investigator. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #137873. 	[Title] Stacked Density Estimation [Abstract] Technical Report No. 97-36, Information and Computer Science Department, University of California, Irvine Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #119956. 	[Title] Function Approximation with Neural Networks and Local Methods: Bias, Variance and Smoothness [Abstract] We review the use of global and local methods for estimating a function mapping R m ) R n from samples of the function containing noise. The relationship between the methods is examined and an empirical comparison is performed using the multi-layer perceptron (MLP) global neural network model, the single nearest-neighbour model, a linear local approximation (LA) model, and the following commonly used datasets: the Mackey-Glass chaotic time series, the Sunspot time series, British English Vowel data, TIMIT speech phonemes, building energy prediction data, and the sonar dataset. We find that the simple local approximation models often outperform the MLP. No criterion such as classification/prediction, size of the training set, dimensionality of the training set, etc. can be used to distinguish whether the MLP or the local approximation method will be superior. However, we find that if we consider histograms of the k-NN density estimates for the training datasets then we can choose the best performing method a priori by selecting local approximation when the spread of the density histogram is large and choosing the MLP otherwise. This result correlates with the hypothesis that the global MLP model is less appropriate when the characteristics of the function to be approximated varies throughout the input space. We discuss the results, the smoothness assumption often made in function approximation, and the bias/variance dilemma. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #151708. 	[Title] Using the Stochastic Gradient Method to Fit Polychotomous Regression Models [Abstract] Technical Report No. 319 April 7, 1997 University of Washington Department of Statistics Seattle, Washington 98195-4322 Abstract Kooperberg, Bose, and Stone (1997) introduced polyclass, a methodology that uses adaptively selected linear splines and their tensor products to model conditional class probabilities. The authors attempted to develop a methodology that would work well on small and moderate size problems and would scale up to large problems. However, the version of polyclass that was developed for large problems was impractical in that it required two months of cpu time to apply it to a large data set. A modification to this methodology involving the use of the stochastic gradient (on-line) method in fitting polyclass models to given sets of basis functions is developed here that makes the methodology applicable to large data sets. In particular, it is successfully applied to a phoneme recognition problem involving 45 phonemes, 81 features, 150,000 cases in the training sample, 1000 basis functions, and 44,000 unknown parameters. Comparisons with neural networks are made both on the original problem and on a three-vowel subproblem. 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 #118436. 	[Title] Performance of On-Line Learning Methods in Predicting Multiprocessor Memory Access Patterns [Abstract] Technical Report UMIACS-TR-96-59 and CS-TR-3676 Institute for Advanced Computer Studies University of Maryland College Park, MD 20742 Abstract Shared memory multiprocessors require reconfigurable interconnection networks (INs) for scalability. These INs are reconfigured by an IN control unit. However, these INs are often plagued by undesirable reconfiguration time that is primarily due to control latency, the amount of time delay that the control unit takes to decide on a desired new IN configuration. To reduce control latency, a trainable prediction unit (PU) was devised and added to the IN controller. The PUs job is to anticipate and reduce control configuration time, the major component of the control latency. Three different on-line prediction techniques were tested to learn and predict repetitive memory access patterns for three typical parallel processing applications, the 2-D relaxation algorithm, matrix multiply and Fast Fourier Transform. The predictions were then used by a routing control algorithm to reduce control latency by configuring the IN to provide needed memory access paths before they were requested. Three prediction techniques were used and tested: 1). a Markov predictor, 2). a linear predictor and 3). a time delay neural network (TDNN) predictor. As expected, different predictors performed best on different applications, however, the TDNN produced the best overall results. 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 #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 #1717. 	[Title] On Convergence Properties of the EM Algorithm for Gaussian Mixtures [Abstract] We build up the mathematical connection between the "Expectation-Maximization" (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix P , and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of P and provide new results analyzing the effect that P has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages and disadvantages of EM and other algorithms for the learning of Gaussian mixture models. This report describes research done at the Center for Biological and Computational Learning and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Center is provided in part by a grant from the National Science Foundation under contract ASC-9217041. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00000-00-A-0000. The authors were also supported by the HK RGC Earmarked Grant CUHK250/94E, by a grant from the McDonnell-Pew Foundation, by a grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, and by grant N00014-90-1-0777 from the Office of Naval Research. Michael I. Jordan is an NSF Presidential Young Investigator. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #299197. 	[Title] TD Learning of Game Evaluation Functions with Hierarchical Neural Architectures [Abstract]  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 #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 #120013. 	[Title] From Isolation to Cooperation: An Alternative View of a System of Experts [Abstract] We introduce a constructive, incremental learning system for regression problems that models data by means of locally linear experts. In contrast to other approaches, the experts are trained independently and do not compete for data during learning. Only when a prediction for a query is required do the experts cooperate by blen ding their individual predictions. Each expert is trained by minimizing a penalized local cross validation error using second order methods. In this way, an expert is able to adjust the size and shape of the receptive field in which its predictions are valid, and also to adjust its bias on the importance of individual input dimensions. The size and shape adjustment corresponds to finding a local distance metric, while the bias adjustment accomplishes local dimensio n-ality reduction. We derive asymptotic results for our method. In a variety of simulations we demonstrate the properties of the algorithm with respect to interference, learning speed, prediction accuracy, feature detection, and task or i-ented incremental learning. 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 #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 #4329. 	[Title] Supervised learning from incomplete data via an EM approach [Abstract] Real-world learning tasks may involve high-dimensional data sets with arbitrary patterns of missing data. In this paper we present a framework based on maximum likelihood density estimation for learning from such data sets. We use mixture models for the density estimates and make two distinct appeals to the Expectation-Maximization (EM) principle (Dempster et al., 1977) in deriving a learning algorithm|EM is used both for the estimation of mixture components and for coping with missing data. The resulting algorithm is applicable to a wide range of supervised as well as unsupervised learning problems. Results from a classification benchmark|the iris data set|are presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4330. 	[Title] Hierarchical Mixtures of Experts and the EM Algorithm [Abstract] We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain. This report describes research done at the Dept. of Brain and Cognitive Sciences, the Center for Biological and Computational Learning, and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for CBCL is provided in part by a grant from the NSF (ASC-9217041). Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Dept. of Defense. The authors were supported by a grant from the McDonnell-Pew Foundation, by a grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, by by grant IRI-9013991 from the National Science Foundation, by grant N00014-90-J-1942 from the Office of Naval Research, and by NSF grant ECS-9216531 to support an Initiative in Intelligent Control at MIT. Michael I. Jordan is a NSF Presidential Young Investigator. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #390889. 	[Title] A HIERARCHICAL COMMUNITY OF EXPERTS [Abstract] We describe a directed acyclic graphical model that contains a hierarchy of linear units and a mechanism for dynamically selecting an appropriate subset of these units to model each observation. The non-linear selection mechanism is a hierarchy of binary units each of which gates the output of one of the linear units. There are no connections from linear units to binary units, so the generative model can be viewed as a logistic belief net (Neal 1992) which selects a skeleton linear model from among the available linear units. We show that Gibbs sampling can be used to learn the parameters of the linear and binary units even when the sampling is so brief that the Markov chain is far from equilibrium. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114352. 	[Title] Cortical Mechanisms of Visual Recognition and Learning: A Hierarchical Kalman Filter Model [Abstract] We describe a biologically plausible model of dynamic recognition and learning in the visual cortex based on the statistical theory of Kalman filtering from optimal control theory. The model utilizes a hierarchical network whose successive levels implement Kalman filters operating over successively larger spatial and temporal scales. Each hierarchical level in the network predicts the current visual recognition state at a lower level and adapts its own recognition state using the residual error between the prediction and the actual lower-level state. Simultaneously, the network also learns an internal model of the spatiotemporal dynamics of the input stream by adapting the synaptic weights at each hierarchical level in order to minimize prediction errors. The Kalman filter model respects key neuroanatomical data such as the reciprocity of connections between visual cortical areas, and assigns specific computational roles to the inter-laminar connections known to exist between neurons in the visual cortex. Previous work elucidated the usefulness of this model in explaining neurophysiological phenomena such as endstopping and other related extra-classical receptive field effects. In this paper, in addition to providing a more detailed exposition of the model, we present a variety of experimental results demonstrating the ability of this model to perform robust spatiotemporal segmentation and recognition of objects and image sequences in the presence of varying amounts of occlusion, background clutter, and noise. 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 #6913. 	[Title] On Convergence of the EM Algorithm and the Gibbs Sampler  SUMMARY [Abstract] In this article we investigate the relationship between the two popular algorithms, the EM algorithm and the Gibbs sampler. We show that the approximate rate of convergence of the Gibbs sampler by Gaussian approximation is equal to that of the corresponding EM type algorithm. This helps in implementing either of the algorithms as improvement strategies for one algorithm can be directly transported to the other. In particular, by running the EM algorithm we know approximately how many iterations are needed for convergence of the Gibbs sampler. We also obtain a result that under conditions, the EM algorithm used for finding the maximum likelihood estimates can be slower to converge than the corresponding Gibbs sampler for Bayesian inference which uses proper prior distributions. We illustrate our results in a number of realistic examples all based on the generalized linear mixed models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1136393. 	[Title] Data-driven Modeling and Synthesis of Acoustical Instruments [Abstract] We present a framework for the analysis and synthesis of acoustical instruments based on data-driven probabilistic inference modeling. Audio time series and boundary conditions of a played instrument are recorded and the non-linear mapping from the control data into the audio space is inferred using the general inference framework of Cluster-Weighted Modeling. The resulting model is used for real-time synthesis of audio sequences from new input data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1136397. 	[Title] State Reconstruction for Determining Predictability in Driven Nonlinear Acoustical Systems [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #69392. 	[Title] Forecasting electricity demand using nonlinear mixture of experts [Abstract] In this paper we study a forecasting model based on mixture of experts for predicting the French electric daily consumption energy. We split the task into two parts. Using mixture of experts, a first model predicts the electricity demand from the exogenous variables (such as temperature and degree of cloud cover) and can be viewed as a nonlinear regression model of mixture of Gaussians. Using a single neural network, a second model predicts the evolution of the residual error of the first one, and can be viewed as an nonlinear autoregression model. We analyze the splitting of the input space generated by the mixture of experts model, and compare the performance to models presently used. 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 #395553. 	[Title] Mixtures of Probabilistic Principal Component Analysers [Abstract] Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a combination of local linear PCA projections. However, conventional PCA does not correspond to a probability density, and so there is no unique way to combine PCA models. Previous attempts to formulate mixture models for PCA have therefore to some extent been ad hoc. In this paper, PCA is formulated within a maximum-likelihood framework, based on a specific form of Gaussian latent variable model. This leads to a well-defined mixture model for probabilistic principal component analysers, whose parameters can be determined using an EM algorithm. We discuss the advantages of this model in the context of clustering, density modelling and local dimensionality reduction, and we demonstrate its application to image compression and handwritten digit recognition. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #69418. 	[Title] Constructive Algorithms for Hierarchical Mixtures of Experts [Abstract] We present two additions to the hierarchical mixture of experts (HME) architecture. We view the HME as a tree structured classifier. Firstly, by applying a likelihood splitting criteria to each expert in the HME we "grow" the tree adaptively during training. Secondly, by considering only the most probable path through the tree we may "prune" branches away, either temporarily, or permanently if they become redundant. We demonstrate results for the growing and pruning algorithms which show significant speed ups and more efficient use of parameters over the conventional algorithms in discriminating between two interlocking spirals and classifying 8-bit parity patterns. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1103676. 	[Title] Memory-based Time Series Recognition  A New Methodology and Real World Applications [Abstract]  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 #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 #11093. 	[Title] Factorial Learning and the EM Algorithm [Abstract] Many real world learning problems are best characterized by an interaction of multiple independent causes or factors. Discovering such causal structure from the data is the focus of this paper. Based on Zemel and Hinton's cooperative vector quantizer (CVQ) architecture, an unsupervised learning algorithm is derived from the Expectation-Maximization (EM) framework. Due to the combinatorial nature of the data generation process, the exact E-step is computationally intractable. Two alternative methods for computing the E-step are proposed: Gibbs sampling and mean-field approximation, and some promising empirical results are presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12638. 	[Title] Soft Classification, a.k.a. Risk Estimation, via Penalized Log Likelihood and Smoothing Spline Analysis of Variance [Abstract]  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 #1112929. 	[Title] Combining Linear Discriminant Functions with Neural Networks for Supervised Learning [Abstract] A novel supervised learning method is presented by combining linear discriminant functions with neural networks. The proposed method results in a tree-structured hybrid architecture. Due to constructive learning, the binary tree hierarchical architecture is automatically generated by a controlled growing process for a specific supervised learning task. Unlike the classic decision tree, the linear discriminant functions are merely employed in the intermediate level of the tree for heuristically partitioning a large and complicated task into several smaller and simpler subtasks in the proposed method. These subtasks are dealt with by component neural networks at the leaves of the tree accordingly. For constructive learning, growing and credit-assignment algorithms are developed to serve for the hybrid architecture. The proposed architecture provides an efficient way to apply existing neural networks (e.g. multi-layered perceptron) for solving a large scale problem. We have already applied the proposed method to a universal approximation problem and several benchmark classification problems in order to evaluate its performance. Simulation results have shown that the proposed method yields better results and faster training in comparison with the multi-layered perceptron. 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 #68463. 	[Title] Cost-sensitive feature reduction applied to a hybrid genetic algorithm [Abstract] This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether irrelevant information can be eliminated in preprocessing before starting the learning process. A case study of data preprocessing for a hybrid genetic algorithm shows that the elimination of irrelevant features can substantially improve the efficiency of learning. In addition, cost-sensitive feature elimination can be effective for reducing costs of induced hypotheses. 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 #1103737. 	[Title] On learning hierarchical classifications [Abstract] Many significant real-world classification tasks involve a large number of categories which are arranged in a hierarchical structure; for example, classifying documents into subject categories under the library of congress scheme, or classifying world-wide-web documents into topic hierarchies. We investigate the potential benefits of using a given hierarchy over base classes to learn accurate multi-category classifiers for these domains. First, we consider the possibility of exploiting a class hierarchy as prior knowledge that can help one learn a more accurate classifier. We explore the benefits of learning category-discriminants in a hard top-down fashion and compare this to a soft approach which shares training data among sibling categories. In doing so, we verify that hierarchies have the potential to improve prediction accuracy. But we argue that the reasons for this can be subtle. Sometimes, the improvement is only because using a hierarchy happens to constrain the expressiveness of a hypothesis class in an appropriate manner. However, various controlled experiments show that in other cases the performance advantage associated with using a hierarchy really does seem to be due to the prior knowledge it encodes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46468. 	[Title] A Statistical Approach to Decision Tree Modeling [Abstract] A statistical approach to decision tree modeling is described. In this approach, each decision in the tree is modeled parametrically as is the process by which an output is generated from an input and a sequence of decisions. The resulting model yields a likelihood measure of goodness of fit, allowing ML and MAP estimation techniques to be utilized. An efficient algorithm is presented to estimate the parameters in the tree. The model selection problem is presented and several alternative proposals are considered. A hidden Markov version of the tree is described for data sequences that have temporal dependencies. 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 #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 #1152917. 	[Title]  [Abstract] Convergence Results for the EM Approach to Abstract The Expectation-Maximization (EM) algorithm is an iterative approach to maximum likelihood parameter estimation. Jordan and Jacobs (1993) recently proposed an EM algorithm for the mixture of experts architecture of Jacobs, Jordan, Nowlan and Hinton (1991) and the hierarchical mixture of experts architecture of Jordan and Jacobs (1992). They showed empirically that the EM algorithm for these architectures yields significantly faster convergence than gradient ascent. In the current paper we provide a theoretical analysis of this algorithm. We show that the algorithm can be regarded as a variable metric algorithm with its searching direction having a positive projection on the gradient of the log likelihood. We also analyze the convergence of the algorithm and provide an explicit expression for the convergence rate. In addition, we describe an acceleration technique that yields a significant speedup in simulation experiments. This report describes research done at the Dept. of Brain and Cognitive Sciences, the Center for Biological and Computational Learning, and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for CBCL is provided in part by a grant from the NSF (ASC-9217041). Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Dept. of Defense. The authors were supported by a grant from the McDonnell-Pew Foundation, by a grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, by by grant IRI-9013991 from the National Science Foundation, by grant N00014-90-J-1942 from the Office of Naval Research, and by NSF grant ECS-9216531 to support an Initiative in Intelligent Control at MIT. Michael I. Jordan is a NSF Presidential Young Investigator. 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 #1110438. 	[Title] A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets [Abstract] A novel method is proposed for combining multiple probabilistic classifiers on different feature sets. In order to achieve the improved classification performance, a generalized finite mixture model is proposed as a linear combination scheme and implemented based on radial basis function networks. In the linear combination scheme, soft competition on different feature sets is adopted as an automatic feature rank mechanism so that different feature sets can be always simultaneously used in an optimal way to determine linear combination weights. For training the linear combination scheme, a learning algorithm is developed based on Expectation-Maximization (EM) algorithm. The proposed method has been applied to a typical real world problem, viz. speaker identification, in which different feature sets often need consideration simultaneously for robustness. Simulation results show that the proposed method yields good performance in speaker identification. 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 #1132459. 	[Title] First experiments using a mixture of nonlinear experts for time series prediction [Abstract] This paper investigates the advantages and disadvantages of the mixture of experts (ME) model (introduced to the connectionist community in [JJNH91] and applied to time series analysis in [WM95]) on two time series where the dynamics is well understood. The first series is a computer-generated series, consisting of a mixture between a noise-free process (the quadratic map) and a noisy process (a composition of a noisy linear autoregressive and a hyperbolic tangent). There are three main results: (1) the ME model produces significantly better results than single networks; (2) it discovers the regimes correctly and also allows us to characterize the sub-processes through their variances. (3) due to the correct matching of the noise level of the model to that of the data it avoids overfitting. The second series is the laser series used in the Santa Fe competition; the ME model also obtains excellent out-of-sample predictions, allows for analysis and shows no overfitting. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1132461. 	[Title] Avoiding overfitting by locally matching the noise level of the data gating network discovers the [Abstract] When trying to forecast the future behavior of a real-world system, two of the key problems are nonstationarity of the process (e.g., regime switching) and overfitting of the model (particularly serious for noisy processes). This articles shows how gated experts can point to solutions to these problems. The architecture, also called society of experts and mixture of experts consists of a (nonlinear) gating network and several (nonlinear) competing experts. Each expert learns a conditional mean (as usual), but each expert also has its own adaptive width. The gating network learns to assign a probability to each expert that depends on the input. This article first discusses the assumptions underlying this architecture and derives the weight update rules. It then evaluates the performance of gated experts in comparison to that of single networks, as well as to networks with two outputs, one predicting the mean, the other one the local error bar. This article also investigates the ability of gated experts to discover and characterize underlying the regimes. The results are: * there is significantly less overfitting compared to single nets, for two reasons: only subsets of the potential inputs are given to the experts and gating network (less of a curse of dimensionality), and the experts learn to match their variances to the (local) noise levels, thus only learning as This article focuses on the architecture and the overfitting problem. Applications to a computer-generated toy problem and the laser data from Santa Fe Competition are given in [Mangeas and Weigend, 1995], and the application to the real-world problem of predicting the electricity demand of France are given in [Mangeas et al., 1995]. much as the data support. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #32688. 	[Title] CLASSIFICATION USING HIERARCHICAL MIXTURES OF EXPERTS [Abstract] There has recently been widespread interest in the use of multiple models for classification and regression in the statistics and neural networks communities. The Hierarchical Mixture of Experts (HME) [1] has been successful in a number of regression problems, yielding significantly faster training through the use of the Expectation Maximisation algorithm. In this paper we extend the HME to classification and results are reported for three common classification benchmark tests: Exclusive-Or, N-input Parity and Two Spirals. 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 #684986. 	[Title] A Hierarchical Latent Variable Model for Data Visualization [Abstract] Visualization has proven to be a powerful and widely-applicable tool for the analysis and interpretation of multi-variate data. Most visualization algorithms aim to find a projection from the data space down to a two-dimensional visualization space. However, for complex data sets living in a high-dimensional space it is unlikely that a single two-dimensional projection can reveal all of the interesting structure. We therefore introduce a hierarchical visualization algorithm which allows the complete data set to be visualized at the top level, with clusters and sub-clusters of data points visualized at deeper levels. The algorithm is based on a hierarchical mixture of latent variable models, whose parameters are estimated using the expectation-maximization algorithm. We demonstrate the principle of the approach on a toy data set, and we then apply the algorithm to the visualization of a synthetic data set in 12 dimensions obtained from a simulation of multi-phase flows in oil pipelines, and to data in 36 dimensions derived from satellite images. A Matlab software implementation of the algorithm is publicly available from the world-wide web. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1109439. 	[Title] Classifying Seismic Signals by Integrating Ensembles of Neural Networks [Abstract] This paper proposes a classification scheme based on integration of multiple Ensembles of ANNs. It is demonstrated on a classification problem, in which seismic signals of Natural Earthquakes must be distinguished from seismic signals of Artificial Explosions. A Redundant Classification Environment consists of several Ensembles of Neural Networks is created and trained on Bootstrap Sample Sets, using various data representations and architectures. The ANNs within the Ensembles are aggregated (as in Bagging) while the Ensembles are integrated non-linearly, in a signal adaptive manner, using a posterior confidence measure based on the agreement (variance) within the Ensembles. The proposed Integrated Classification Machine achieved 92.1% correct classifications on the seismic test data. Cross Validation evaluations and comparisons indicate that such integration of a collection of ANN's Ensembles is a robust way for handling high dimensional problems with a complex non-stationary signal space as in the current Seismic Classification problem. 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 #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 #35797. 	[Title] Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules [Abstract] Proben1 is a collection of problems for neural network learning in the realm of pattern classification and function approximation plus a set of rules and conventions for carrying out benchmark tests with these or similar problems. Proben1 contains 15 data sets from 12 different domains. All datasets represent realistic problems which could be called diagnosis tasks and all but one consist of real world data. The datasets are all presented in the same simple format, using an attribute representation that can directly be used for neural network training. Along with the datasets, Proben1 defines a set of rules for how to conduct and how to document neural network benchmarking. The purpose of the problem and rule collection is to give researchers easy access to data for the evaluation of their algorithms and networks and to make direct comparison of the published results feasible. This report describes the datasets and the benchmarking rules. It also gives some basic performance measures indicating the difficulty of the various problems. These measures can be used as baselines for comparison. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #168410. 	[Title] A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar [Abstract] Compositional Q-Learning (CQ-L) (Singh 1992) is a modular approach to learning to perform composite tasks made up of several elemental tasks by reinforcement learning. Skills acquired while performing elemental tasks are also applied to solve composite tasks. Individual skills compete for the right to act and only winning skills are included in the decomposition of the composite task. We extend the original CQ-L concept in two ways: (1) a more general reward function, and (2) the agent can have more than one actuator. We use the CQ-L architecture to acquire skills for performing composite tasks with a simulated two-linked manipulator having large state and action spaces. The manipulator is a non-linear dynamical system and we require its end-effector to be at specific positions in the workspace. Fast function approximation in each of the Q-modules is achieved through the use of an array of Cerebellar Model Articulation Controller (CMAC) (Albus Our research interests involve the scaling up of machine learning methods, especially reinforcement learning, for autonomous robot control. We are interested in function approximators suitable for reinforcement learning in problems with large state spaces, such as the Cerebellar Model Articulation Controller (CMAC) (Albus 1975) which permit fast, online learning and good local generalization. In addition, we are interested in task decomposition by reinforcement learning and the use of hierarchical and modular function approximator architectures. We are examining the effectiveness of a modified Hierarchical Mixtures of Experts (HME) (Jordan & Jacobs 1993) approach for reinforcement learning since the original HME was developed mainly for supervised learning and batch learning tasks. The incorporation of domain knowledge into reinforcement learning agents is an important way of extending their capabilities. Default policies can be specified, and domain knowledge can also be used to restrict the size of the state-action space, leading to faster learning. We are investigating the use of Q-Learning (Watkins 1989) in planning tasks, using a classifier system (Holland 1986) to encode the necessary condition-action rules. Jordan, M. & Jacobs, R. (1993), Hierarchical mixtures of experts and the EM algorithm, Technical Report 9301, MIT Computational Cognitive Science. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #949217. 	[Title] Comparison of Neural and Statistical Classifiers| Theory and Practice [Abstract] Research Reports A13 January 1996 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 #8699. 	[Title] Theory Revision in Fault Hierarchies [Abstract] The fault hierarchy representation is widely used in expert systems for the diagnosis of complex mechanical devices. On the assumption that an appropriate bias for a knowledge representation language is also an appropriate bias for learning in this domain, we have developed a theory revision method that operates directly on a fault hierarchy. This task presents several challenges: A typical training instance is missing most feature values, and the pattern of missing features is significant, rather than merely an effect of noise. Moreover, the accuracy of a candidate theory is measured by considering both the sequence of tests required to arrive at a diagnosis and its agreement with the diagnostic endpoints provided by an expert. This paper first describes the algorithm for theory revision of fault hierarchies that was designed to address these challenges, then discusses its application in knowledge base maintenance and reports on experiments that use to revise a fielded diagnostic system. 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 #328370 has the title `Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle  
` and the 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. 
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Question: Which category should Paper #328370 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #328370 has the title `Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle  
` and the 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. 
`.
Question: Which category should Paper #328370 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