Above are papers related to the following paper: [Paper_ID] Paper #235776. 	[Title] Recursive Automatic Algorithm Selection for Inductive Learning [Abstract] COINS Technical Report 94-61 August 1994 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6925. 	[Title] Solving the Multiple-Instance Problem with Axis-Parallel Rectangles [Abstract] The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple-instance problem. Algorithms that ignore the multiple instance problem perform very poorly. An algorithm that directly confronts the multiple instance problem (by attempting to identify which feature vectors are responsible for the observed classifications) performs best, giving 89% correct predictions on a musk-odor prediction task. The paper also illustrates the use of artificial data to debug and compare these algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3218. 	[Title] Rule Induction with CN2: Some Recent Improvements [Abstract] The CN2 algorithm induces an ordered list of classification rules from examples using entropy as its search heuristic. In this short paper, we describe two improvements to this algorithm. Firstly, we present the use of the Laplacian error estimate as an alternative evaluation function and secondly, we show how unordered as well as ordered rules can be generated. We experimentally demonstrate significantly improved performances resulting from these changes, thus enhancing the usefulness of CN2 as an inductive tool. Comparisons with Quinlan's C4.5 are also made. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #69397. 	[Title] Dynamical Selection of Learning Algorithms [Abstract] Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain have met with limited success. This paper proposes a new approach to predicting a given example's class by locating it in the "example space" and then choosing the best learner(s) in that region of the example space to make predictions. The regions of the example space are defined by the prediction patterns of the learners being used. The learner(s) chosen for prediction are selected according to their past performance in that region. This dynamic approach to learning algorithm selection is compared to other methods for selecting from multiple learning algorithms. The approach is then extended to weight rather than select the algorithms according to their past performance in a given region. Both approaches are further evaluated on a set of Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain (e.g. [Aha92, Breiman84]) or for a portion of the domain ([Brodley93, Brodley94]) have met with limited success. This paper proposes a new approach that dynamically selects a learning algorithm for each example by locating it in the "example space" and then choosing the best learner(s) for prediction in that part of the example space. The regions of the example space are formed by the observed prediction patterns of the learners being used. The learner(s) chosen for prediction are selected according to their past performance in that region which is defined by the "cross-validation history." This paper introduces DS, a method for the dynamic selection of a learning algorithm(s). We call it "dynamic" because the learning algorithm(s) used to classify a novel example depends on that example. Preliminary experimentation motivated DW, an extension to DS that dynamically weights the learners predictions according to their regional accuracy. Further experimentation compares DS and DW to a collection of other meta-learning strategies such as cross-validation ([Breiman84]) and various forms of stacking ([Wolpert92]). In this phase of the experiementation, the meta-learners have six constituent learners which are heterogeneous in their search and representation methods (e.g. a rule learner, CN2 [Clark89]; a decision tree learner, C4.5 [Quinlan93]; an oblique decision tree learner, OC1 [Murthy93]; an instance-based learner, PEBLS [Cost93]; a k-nearest neighbor learner, ten domains and compared to several other meta-learning strategies. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #45212. 	[Title] Gain Adaptation Beats Least Squares? [Abstract] I present computational results suggesting that gain-adaptation algorithms based in part on connectionist learning methods may improve over least squares and other classical parameter-estimation methods for stochastic time-varying linear systems. The new algorithms are evaluated with respect to classical methods along three dimensions: asymptotic error, computational complexity, and required prior knowledge about the system. The new algorithms are all of the same order of complexity as LMS methods, O(n), where n is the dimensionality of the system, whereas least-squares methods and the Kalman filter are O(n 2 ). The new methods also improve over the Kalman filter in that they do not require a complete statistical model of how the system varies over time. In a simple computational experiment, the new methods are shown to produce asymptotic error levels near that of the optimal Kalman filter and significantly below those of least-squares and LMS methods. The new methods may perform better even than the Kalman filter if there is any error in the filter's model of how the system varies over time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1115677. 	[Title] Comparison of Regression Methods, Symbolic Induction Methods and Neural Networks in Morbidity Diagnosis and Mortality [Abstract] Classifier induction algorithms differ on what inductive hypotheses they can represent, and on how they search their space of hypotheses. No classifier is better than another for all problems: they have selective superiority. This paper empirically compares six classifier induction algorithms on the diagnosis of equine colic and the prediction of its mortality. The classification is based on simultaneously analyzing sixteen features measured from a patient. The relative merits of the algorithms (linear regression, decision trees, nearest neighbor classifiers, the Model Class Selection system, logistic regression (with and without feature selection), and neural nets) are qualitatively discussed, and the generalization accuracies quantitatively analyzed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8224. 	[Title] Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine [Abstract] Ourston and Mooney, 1990b ] D. Ourston and R. J. Mooney. Improving shared rules in multiple category domain theories. Technical Report AI90-150, Artificial Intelligence Labora tory, University of Texas, Austin, TX, December 1990. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106849. 	[Title] EE380L:Neural Networks for Pattern Recognition  POp Trees  under the guidance of [Abstract] Decision Trees have been widely used for classification/regression tasks. They are relatively much faster to build as compared to Neural Networks and are understandable by humans. In normal decision trees, based on the input vector, only one branch is followed. In Probabilistic OPtion trees, based on the input vector we follow all of the subtrees with some probability. These probabilities are learned by the system. Probabilistic decisions are likely to be useful, when the boundary of classes submerge in each other, or when there is noise in the input data. In addition they provide us with a confidence measure. We allow option nodes in our trees, Again, instead of uniform voting, we learn the weightage of every subtree. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22563. 	[Title] A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping [Abstract] The performance of the error backpropagation (BP) and ID3 learning algorithms was compared on the task of mapping English text to phonemes and stresses. Under the distributed output code developed by Sejnowski and Rosenberg, it is shown that BP consistently out-performs ID3 on this task by several percentage points. Three hypotheses explaining this difference were explored: (a) ID3 is overfitting the training data, (b) BP is able to share hidden units across several output units and hence can learn the output units better, and (c) BP captures statistical information that ID3 does not. We conclude that only hypothesis (c) is correct. By augmenting ID3 with a simple statistical learning procedure, the performance of BP can be approached but not matched. More complex statistical procedures can improve the performance of both BP and ID3 substantially. A study of the residual errors suggests that there is still substantial room for improvement in learning methods for text-to-speech mapping. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6311. 	[Title] Dynamic Automatic Model Selection [Abstract] COINS Technical Report 92-30 February 1992 Abstract The problem of how to learn from examples has been studied throughout the history of machine learning, and many successful learning algorithms have been developed. A problem that has received less attention is how to select which algorithm to use for a given learning task. The ability of a chosen algorithm to induce a good generalization depends on how appropriate the model class underlying the algorithm is for the given task. We define an algorithm's model class to be the representation language it uses to express a generalization of the examples. Supervised learning algorithms differ in their underlying model class and in how they search for a good generalization. Given this characterization, it is not surprising that some algorithms find better generalizations for some, but not all tasks. Therefore, in order to find the best generalization for each task, an automated learning system must search for the appropriate model class in addition to searching for the best generalization within the chosen class. This thesis proposal investigates the issues involved in automating the selection of the appropriate model class. The presented approach has two facets. Firstly, the approach combines different model classes in the form of a model combination decision tree, which allows the best representation to be found for each subconcept of the learning task. Secondly, which model class is the most appropriate is determined dynamically using a set of heuristic rules. Explicit in each rule are the conditions in which a particular model class is appropriate and if it is not, what should be done next. In addition to describing the approach, this proposal describes how the approach will be evaluated in order to demonstrate that it is both an efficient and effective method for automatic model selection. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46887. 	[Title] Learning Polynomial Functions by Feature Construction [Abstract] We present a method for learning higher-order polynomial functions from examples using linear regression and feature construction. Regression is used on a set of training instances to produce a weight vector for a linear function over the feature set. If this hypothesis is imperfect, a new feature is constructed by forming the product of the two features that most effectively predict the squared error of the current hypothesis. The algorithm is then repeated. In an extension to this method, the specific pair of features to combine is selected by measuring their joint ability to predict the hypothesis' error. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106103. 	[Title] Bias and the Probability of Generalization [Abstract] In order to be useful, a learning algorithm must be able to generalize well when faced with inputs not previously presented to the system. A bias is necessary for any generalization, and as shown by several researchers in recent years, no bias can lead to strictly better generalization than any other when summed over all possible functions or applications. This paper provides examples to illustrate this fact, but also explains how a bias or learning algorithm can be better than another in practice when the probability of the occurrence of functions is taken into account. It shows how domain knowledge and an understanding of the conditions under which each learning algorithm performs well can be used to increase the probability of accurate generalization, and identifies several of the conditions that should be considered when attempting to select an appropriate bias for a particular problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120444. 	[Title] Systematic Evaluation of Design Decisions in CBR Systems [Abstract] Two important goals in the evaluation of an AI theory or model are to assess the merit of the design decisions in the performance of an implemented computer system and to analyze the impact in the performance when the system faces problem domains with different characteristics. This is particularly difficult in case-based reasoning systems because such systems are typically very complex, as are the tasks and domains in which they operate. We present a methodology for the evaluation of case-based reasoning systems through systematic empirical experimentation over a range of system configurations and environmental conditions, coupled with rigorous statistical analysis of the results of the experiments. This methodology enables us to understand the behavior of the system in terms of the theory and design of the computational model, to select the best system configuration for a given domain, and to predict how the system will behave in response to changing domain and problem characteristics. A case study of a mul-tistrategy case-based and reinforcement learning system which performs autonomous robotic navigation is presented as an example. 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 #1153106. 	[Title] Classification of EEG Signals Using a Sparse Polynomial Builder [Abstract] Edward S. Orosz and Charles W. Anderson Technical Report CS-94-111 April 27, 1994 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #389715. 	[Title] Constructing Nominal Xof-N Attributes [Abstract] Most constructive induction researchers focus only on new boolean attributes. This paper reports a new constructive induction algorithm, called XofN, that constructs new nominal attributes in the form of Xof-N representations. An Xof-N is a set containing one or more attribute-value pairs. For a given instance, its value corresponds to the number of its attribute-value pairs that are true. The promising preliminary experimental results, on both artificial and real-world domains, show that constructing new nominal attributes in the form of Xof-N representations can significantly improve the performance of selective induction in terms of both higher prediction accuracy and lower theory complexity. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1118823. 	[Title] Constructing Conjunctions using Systematic Search on Decision Trees [Abstract] This paper investigates a dynamic path-based method for constructing conjunctions as new attributes for decision tree learning. It searches for conditions (attribute-value pairs) from paths to form new attributes. Compared with other hypothesis-driven new attribute construction methods, the new idea of this method is that it carries out systematic search with pruning over each path of a tree to select conditions for generating a conjunction. Therefore, conditions for constructing new attributes are dynamically decided during search. Empirically evaluation in a set of artificial and real-world domains shows that the dynamic path-based method can improve the performance of selective decision tree learning in terms of both higher prediction accuracy and lower theory complexity. In addition, it shows some performance advantages over a fixed path-based method and a fixed rule-based method for learning decision trees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20584. 	[Title] Multivariate versus Univariate Decision Trees [Abstract] COINS Technical Report 92-8 January 1992 Abstract In this paper we present a new multivariate decision tree algorithm LMDT, which combines linear machines with decision trees. LMDT constructs each test in a decision tree by training a linear machine and then eliminating irrelevant and noisy variables in a controlled manner. To examine LMDT's ability to find good generalizations we present results for a variety of domains. We compare LMDT empirically to a univariate decision tree algorithm and observe that when multivariate tests are the appropriate bias for a given data set, LMDT finds small accurate trees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1111788. 	[Title] Multivariate Decision Trees [Abstract] COINS Technical Report 92-82 December 1992 Abstract Multivariate decision trees overcome a representational limitation of univariate decision trees: univariate decision trees are restricted to splits of the instance space that are orthogonal to the feature's axis. This paper discusses the following issues for constructing multivariate decision trees: representing a multivariate test, including symbolic and numeric features, learning the coefficients of a multivariate test, selecting the features to include in a test, and pruning of multivariate decision trees. We present some new and review some well-known methods for forming multivariate decision trees. The methods are compared across a variety of learning tasks to assess each method's ability to find concise, accurate decision trees. The results demonstrate that some multivariate methods are more effective than others. In addition, the experiments confirm that allowing multivariate tests improves the accuracy of the resulting decision tree over univariate trees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6767. 	[Title] Generalizing from Case Studies: A Case Study [Abstract] Most empirical evaluations of machine learning algorithms are case studies evaluations of multiple algorithms on multiple databases. Authors of case studies implicitly or explicitly hypothesize that the pattern of their results, which often suggests that one algorithm performs significantly better than others, is not limited to the small number of databases investigated, but instead holds for some general class of learning problems. However, these hypotheses are rarely supported with additional evidence, which leaves them suspect. This paper describes an empirical method for generalizing results from case studies and an example application. This method yields rules describing when some algorithms significantly outperform others on some dependent measures. Advantages for generalizing from case studies and limitations of this particular approach are also described. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20592. 	[Title] Learning NonLinearly Separable Boolean Functions With Linear Threshold Unit Trees and Madaline-Style Networks [Abstract] This paper investigates an algorithm for the construction of decisions trees comprised of linear threshold units and also presents a novel algorithm for the learning of non-linearly separable boolean functions using Madaline-style networks which are isomorphic to decision trees. The construction of such networks is discussed, and their performance in learning is compared with standard BackPropagation on a sample problem in which many irrelevant attributes are introduced. Littlestone's Winnow algorithm is also explored within this architecture as a means of learning in the presence of many irrelevant attributes. The learning ability of this Madaline-style architecture on nonoptimal (larger than necessary) networks is also explored. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20593. 	[Title] Generating Neural Networks Through the Induction of Threshold Logic Unit Trees (Extended Abstract) [Abstract] We investigate the generation of neural networks through the induction of binary trees of threshold logic units (TLUs). Initially, we describe the framework for our tree construction algorithm and how such trees can be transformed into an isomorphic neural network topology. Several methods for learning the linear discriminant functions at each node of the tree structure are examined and shown to produce accuracy results that are comparable to classical information theoretic methods for constructing decision trees (which use single feature tests at each node). Our TLU trees, however, are smaller and thus easier to understand. Moreover, we show that it is possible to simultaneously learn both the topology and weight settings of a neural network simply using the training data set that we are given. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114864. 	[Title] Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1) [Abstract] This is a brief annotated bibliography that I wanted to make available to the attendees of my Machine Learning tutorial at the 1995 AI & Statistics Workshop. These slides are available in my WWW pages under slides. Please contact me if you have any questions. Please also note the date (listed above) on which this was most recently updated. While I plan to make occasional updates to this file, it is bound to be outdated quickly. Also, I apologize for the lack of figures, but my time on this project is limited and the slides should compensate. Finally, this bibliography is, by definition, This book is now out of date. Both Pat Langley and Tom Mitchell are in the process of writing textbooks on this subject, but we're still waiting for them. Until then, I suggest looking at both the Readings and the recent ML conference proceedings (both International and European). There are also a few introductory papers on this subject, though I haven't gotten around to putting them in here yet. However, Pat Langley and Dennis Kibler (1988) have written a good paper on ML as an empirical science, and Pat has written several editorials of use to the ML author (Langley 1986; 1987; 1990). incomplete, and I've left out many other references that may be of some use. Please summarise all the given information.

Paper #235776 has the title `Recursive Automatic Algorithm Selection for Inductive Learning  
` and the abstract `COINS Technical Report 94-61 August 1994 
`.
Question: Which category should Paper #235776 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #235776 has the title `Recursive Automatic Algorithm Selection for Inductive Learning  
` and the abstract `COINS Technical Report 94-61 August 1994 
`.
Question: Which category should Paper #235776 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: Theory