Above are papers related to the following paper: [Paper_ID] Paper #3233. 	[Title] A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection [Abstract] We review accuracy estimation methods and compare the two most common methods: cross-validation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leave-one-out cross-validation. We report on a large-scale experiment|over half a million runs of C4.5 and a Naive-Bayes algorithm|to estimate the effects of different parameters on these algorithms on real-world datasets. For cross-validation, we vary the number of folds and whether the folds are stratified or not; for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, the best method to use for model selection is ten-fold stratified cross validation, even if computation power allows using more folds. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131137. 	[Title] Mining and Model Simplicity: A Case Study in Diagnosis [Abstract] Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD), 1996. The official version of this paper has been published by the American Association for Artificial Intelligence (http://www.aaai.org) c fl 1996, American Association for Artificial Intelligence. All rights reserved. Abstract We describe the results of performing data mining on a challenging medical diagnosis domain, acute abdominal pain. This domain is well known to be difficult, yielding little more than 60% predictive accuracy for most human and machine diagnosticians. Moreover, many researchers argue that one of the simplest approaches, the naive Bayesian classifier, is optimal. By comparing the performance of the naive Bayesian classifier to its more general cousin, the Bayesian network classifier, and to selective Bayesian classifiers with just 10% of the total attributes, we show that the simplest models perform at least as well as the more complex models. We argue that simple models like the selective naive Bayesian classifier will perform as well as more complicated models for similarly complex domains with relatively small data sets, thereby calling into question the extra expense necessary to induce more complex models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110531. 	[Title] Computational Learning in Humans and Machines [Abstract] In this paper we review research on machine learning and its relation to computational models of human learning. We focus initially on concept induction, examining five main approaches to this problem, then consider the more complex issue of learning sequential behaviors. After this, we compare the rhetoric that sometimes appears in the machine learning and psychological literature with the growing evidence that different theoretical paradigms typically produce similar results. In response, we suggest that concrete computational models, which currently dominate the field, may be less useful than simulations that operate at a more abstract level. We illustrate this point with an abstract simulation that explains a challenging phenomenon in the area of category learning, and we conclude with some general observations about such abstract models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #45188. 	[Title] A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with [Abstract] We give an analysis of the generalization error of cross validation in terms of two natural measures of the difficulty of the problem under consideration: the approximation rate (the accuracy to which the target function can be ideally approximated as a function of the number of hypothesis parameters), and the estimation rate (the deviation between the training and generalization errors as a function of the number of hypothesis parameters). The approximation rate captures the complexity of the target function with respect to the hypothesis model, and the estimation rate captures the extent to which the hypothesis model suffers from overfitting. Using these two measures, we give a rigorous and general bound on the error of cross validation. The bound clearly shows the tradeoffs involved with making fl the fraction of data saved for testing too large or too small. By optimizing the bound with respect to fl, we then argue (through a combination of formal analysis, plotting, and controlled experimentation) that the following qualitative properties of cross validation behavior should be quite robust to significant changes in the underlying model selection problem: Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #45189. 	[Title] An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the [Abstract] We investigate the problem of model selection in the setting of supervised learning of boolean functions from independent random examples. More precisely, we compare methods for finding a balance between the complexity of the hypothesis chosen and its observed error on a random training sample of limited size, when the goal is that of minimizing the resulting generalization error. We undertake a detailed comparison of three well-known model selection methods | a variation of Vapnik's Guaranteed Risk Minimization (GRM), an instance of Rissanen's Minimum Description Length Principle (MDL), and cross validation (CV). We introduce a general class of model selection methods (called penalty-based methods) that includes both GRM and MDL, and provide general methods for analyzing such rules. We provide both controlled experimental evidence and formal theorems to support the following conclusions: * The class of penalty-based methods is fundamentally handicapped in the sense that there exist two types of model selection problems for which every penalty-based method must incur large generalization error on at least one, while CV enjoys small generalization error Despite the inescapable incomparability of model selection methods under certain circumstances, we conclude with a discussion of our belief that the balance of the evidence provides specific reasons to prefer CV to other methods, unless one is in possession of detailed problem-specific information. on both. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2440. 	[Title] Quinlan, 1990 J.R. Quinlan. Learning logical definitions from relations. Machine Learning, First-order theory revision. In [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27530. 	[Title] Learning Symbolic Rules Using Artificial Neural Networks [Abstract] A distinct advantage of symbolic learning algorithms over artificial neural networks is that typically the concept representations they form are more easily understood by humans. One approach to understanding the representations formed by neural networks is to extract symbolic rules from trained networks. In this paper we describe and investigate an approach for extracting rules from networks that uses (1) the NofM extraction algorithm, and (2) the network training method of soft weight-sharing. Previously, the NofM algorithm had been successfully applied only to knowledge-based neural networks. Our experiments demonstrate that our extracted rules generalize better than rules learned using the C4.5 system. In addition to being accurate, our extracted rules are also reasonably comprehensible. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #36620. 	[Title] Towards a Better Understanding of Memory-Based Reasoning Systems [Abstract] We quantify both experimentally and analytically the performance of memory-based reasoning (MBR) algorithms. To start gaining insight into the capabilities of MBR algorithms, we compare an MBR algorithm using a value difference metric to a popular Bayesian classifier. These two approaches are similar in that they both make certain independence assumptions about the data. However, whereas MBR uses specific cases to perform classification, Bayesian methods summarize the data probabilistically. We demonstrate that a particular MBR system called Pebls works comparatively well on a wide range of domains using both real and artificial data. With respect to the artificial data, we consider distributions where the concept classes are separated by functional discriminants, as well as time-series data generated by Markov models of varying complexity. Finally, we show formally that Pebls can learn (in the limit) natural concept classes that the Bayesian classifier cannot learn, and that it will attain perfect accuracy whenever Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8079. 	[Title] Bias Plus Variance Decomposition for Zero-One Loss Functions [Abstract] We present a bias-variance decomposition of expected misclassification rate, the most commonly used loss function in supervised classification learning. The bias-variance decomposition for quadratic loss functions is well known and serves as an important tool for analyzing learning algorithms, yet no decomposition was offered for the more commonly used zero-one (misclassification) loss functions until the recent work of Kong & Dietterich (1995) and Breiman (1996). Their decomposition suffers from some major shortcomings though (e.g., potentially negative variance), which our decomposition avoids. We show that, in practice, the naive frequency-based estimation of the decomposition terms is by itself biased and show how to correct for this bias. We illustrate the decomposition on various algorithms and datasets from the UCI repository. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1125393. 	[Title] Beyond predictive accuracy: what? [Abstract] Today's potential users of machine learning technology are faced with the non-trivial problem of choosing, from the large, ever-increasing number of available tools, the one most appropriate for their particular task. To assist the often non-initiated users, it is desirable that this model selection process be automated. Using experience from base level learning, researchers have proposed meta-learning as a possible solution. Historically, predictive accuracy has been the de facto criterion, with most work in meta-learning focusing on the discovery of rules that match applications to models based on accuracy only. Although predictive accuracy is clearly an important criterion, it is also the case that there are a number of other criteria that could, and often ought to, be considered when learning about model selection. This paper presents a number of such criteria and discusses the impact they have on meta-level approaches to model selection. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #49811. 	[Title] Estimating the Accuracy of Learned Concepts [Abstract] This paper investigates alternative estimators of the accuracy of concepts learned from examples. In particular, the cross-validation and 632 bootstrap estimators are studied, using synthetic training data and the foil learning algorithm. Our experimental results contradict previous papers in statistics, which advocate the 632 bootstrap method as superior to cross-validation. Nevertheless, our results also suggest that conclusions based on cross-validation in previous machine learning papers are unreliable. Specifically, our observations are that (i) the true error of the concept learned by foil from independently drawn sets of examples of the same concept varies widely, (ii) the estimate of true error provided by cross-validation has high variability but is approximately unbiased, and (iii) the 632 bootstrap estimator has lower variability than cross-validation, but is systematically biased. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3222. 	[Title] Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier [Abstract] The simple Bayesian classifier (SBC) is commonly thought to assume that attributes are independent given the class, but this is apparently contradicted by the surprisingly good performance it exhibits in many domains that contain clear attribute dependences. No explanation for this has been proposed so far. In this paper we show that the SBC does not in fact assume attribute independence, and can be optimal even when this assumption is violated by a wide margin. The key to this finding lies in the distinction between classification and probability estimation: correct classification can be achieved even when the probability estimates used contain large errors. We show that the previously-assumed region of optimality of the SBC is a second-order infinitesimal fraction of the actual one. This is followed by the derivation of several necessary and several sufficient conditions for the optimality of the SBC. For example, the SBC is optimal for learning arbitrary conjunctions and disjunctions, even though they violate the independence assumption. The paper also reports empirical evidence of the SBC's competitive performance in domains containing substantial degrees of attribute dependence. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #964248. 	[Title] Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier [Abstract] The simple Bayesian classifier (SBC), sometimes called Naive-Bayes, is built based on a conditional independence model of each attribute given the class. The model was previously shown to be surprisingly robust to obvious violations of this independence assumption, yielding accurate classification models even when there are clear conditional dependencies. The SBC can serve as an excellent tool for initial exploratory data analysis when coupled with a visualizer that makes its structure comprehensible. We describe such a visual representation of the SBC model that has been successfully implemented. We describe the requirements we had for such a visualization and the design decisions we made to satisfy them. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #976284. 	[Title] Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation [Abstract] In this paper we prove sanity-check bounds for the error of the leave-one-out cross-validation estimate of the generalization error: that is, bounds showing that the worst-case error of this estimate is not much worse than that of the training error estimate. The name sanity-check refers to the fact that although we often expect the leave-one-out estimate to perform considerably better than the training error estimate, we are here only seeking assurance that its performance will not be considerably worse. Perhaps surprisingly, such assurance has been given only for rather limited cases in the prior literature on cross-validation. Any nontrivial bound on the error of leave-one-out must rely on some notion of algorithmic stability. Previous bounds relied on the rather strong notion of hypothesis stability, whose application was primarily limited to nearest-neighbor and other local algorithms. Here we introduce the new and weaker notion of error stability, and apply it to obtain sanity-check bounds for leave-one-out for other classes of learning algorithms, including training error minimization procedures and Bayesian algorithms. We also provide lower bounds demonstrating the necessity of error stability for proving bounds on the error of the leave-one-out estimate, and the fact that for training error minimization algorithms, in the worst case such bounds must still depend on the Vapnik-Chervonenkis dimension of the hypothesis class. 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 #3232. 	[Title] The Power of Decision Tables [Abstract] We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on artificial and real-world domains containing only discrete features, IDTM, an algorithm inducing decision tables, can sometimes outperform state-of-the-art algorithms such as C4.5. Surprisingly, performance is quite good on some datasets with continuous features, indicating that many datasets used in machine learning either do not require these features, or that these features have few values. We also describe an incremental method for performing cross-validation that is applicable to incremental learning algorithms including IDTM. Using incremental cross-validation, it is possible to cross-validate a given dataset and IDTM in time that is linear in the number of instances, the number of features, and the number of label values. The time for incremental cross-validation is independent of the number of folds chosen, hence leave-one-out cross-validation and ten-fold cross-validation take the same time. 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 #3235. 	[Title] Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid [Abstract] Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classification tasks even when the conditional independence assumption on which they are based is violated. However, most studies were done on small databases. We show that in some larger databases, the accuracy of Naive-Bayes does not scale up as well as decision trees. We then propose a new algorithm, NBTree, which induces a hybrid of decision-tree classifiers and Naive-Bayes classifiers: the decision-tree nodes contain uni-variate splits as regular decision-trees, but the leaves contain Naive-Bayesian classifiers. The approach retains the interpretability of Naive-Bayes and decision trees, while resulting in classifiers that frequently outperform both constituents, especially in the larger databases tested. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3236. 	[Title] MLC A Machine Learning Library in C [Abstract] We present MLC ++ , a library of C ++ classes and tools for supervised Machine Learning. While MLC ++ provides general learning algorithms that can be used by end users, the main objective is to provide researchers and experts with a wide variety of tools that can accelerate algorithm development, increase software reliability, provide comparison tools, and display information visually. More than just a collection of existing algorithms, MLC ++ is an attempt to extract commonalities of algorithms and decompose them for a unified view that is simple, coherent, and extensible. In this paper we discuss the problems MLC ++ aims to solve, the design of MLC ++ , and the current functionality. 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 #1109542. 	[Title] The covariance inflation criterion for adaptive model selection [Abstract] We propose a new criterion for model selection in prediction problems. The covariance inflation criterion adjusts the training error by the average covariance of the predictions and responses, when the prediction rule is applied to permuted versions of the dataset. This criterion can be applied to general prediction problems (for example regression or classification), and to general prediction rules (for example stepwise regression, tree-based models and neural nets). As a byproduct we obtain a measure of the effective number of parameters used by an adaptive procedure. We relate the covariance inflation criterion to other model selection procedures and illustrate its use in some regression and classification problems. We also revisit the conditional bootstrap approach to model selection. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3240. 	[Title] An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms [Abstract] In this paper we present an average-case analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noise-free Boolean attributes. We calculate the probability that the algorithm will induce an arbitrary pair of concept descriptions and then use this to compute the probability of correct classification over the instance space. The analysis takes into account the number of training instances, the number of attributes, the distribution of these attributes, and the level of class noise. We also explore the behavioral implications of the analysis by presenting predicted learning curves for artificial domains, and give experimental results on these domains as a check on our reasoning. One goal of research in machine learning is to discover principles that relate algorithms and domain characteristics to behavior. To this end, many researchers have carried out systematic experimentation with natural and artificial domains in search of empirical regularities (e.g., Kibler & Langley, 1988). Others have focused on theoretical analyses, often within the paradigm of probably approximately correct learning (e.g., Haus-sler, 1990). However, most experimental studies are based only on informal analyses of the learning task, whereas most formal analyses address the worst case, and thus bear little relation to empirical results. ber of attributes, and the class and attribute frequencies, they obtain predictions about the behavior of induction algorithms and used experiments to check their analyses. 1 However, their research does not focus on algorithms typically used by the experimental and practical sides of machine learning, and it is important that average-case analyses be extended to such methods. Recently, there has been growing interest in probabilistic approaches to inductive learning. For example, Fisher (1987) has described Cobweb, an incremental algorithm for conceptual clustering that draws heavily on Bayesian ideas, and the literature reports a number of systems that build on this work (e.g., Allen & Lang-ley, 1990; Iba & Gennari, 1991; Thompson & Langley, 1991). Cheeseman et al. (1988) have outlined Auto-Class, a nonincremental system that uses Bayesian methods to cluster instances into groups, and other researchers have focused on the induction of Bayesian inference networks (e.g., Cooper & Kerskovits, 1991). These recent Bayesian learning algorithms are complex and not easily amenable to analysis, but they share a common ancestor that is simpler and more tractable. This supervised algorithm, which we refer to simply as a Bayesian classifier, comes originally from work in pattern recognition (Duda & Hart, 1973). The method stores a probabilistic summary for each class; this summary contains the conditional probability of each attribute value given the class, as well as the probability (or base rate) of the class. This data structure approximates the representational power of a perceptron; it describes a single decision boundary through the instance space. When the algorithm encounters a new instance, it updates the probabilities stored with the specified class. Neither the order of training instances nor the occurrence of classification errors have any effect on this process. When given a test instance, the classifier uses an evaluation function (which we describe in detail later) to rank the alter Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #25772. 	[Title] Flexible Metric Nearest Neighbor Classiflcation [Abstract] The K-nearest-neighbor decision rule assigns an object of unknown class to the plurality class among the K labeled "training" objects that are closest to it. Closeness is usually deflned in terms of a metric distance on the Euclidean space with the input measurement variables as axes. The metric chosen to deflne this distance can strongly efiect performance. An optimal choice depends on the problem at hand as characterized by the respective class distributions on the input measurement space, and within a given problem, on the location of the unknown object in that space. In this paper new types of K-nearest-neighbor procedures are described that estimate the local relevance of each input variable, or their linear combinations, for each individual point to be classifled. This information is then used to separately customize the metric used to deflne distance from that object in flnding its nearest neighbors. These procedures are a hybrid between regular K-nearest-neighbor methods and treestructured recursive partitioning techniques popular in statistics and machine learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #36145. 	[Title] Generating Accurate and Diverse Members of a Neural-Network Ensemble [Abstract] Neural-network ensembles have been shown to be very accurate classification techniques. Previous work has shown that an effective ensemble should consist of networks that are not only highly correct, but ones that make their errors on different parts of the input space as well. Most existing techniques, however, only indirectly address the problem of creating such a set of networks. In this paper we present a technique called Addemup that uses genetic algorithms to directly search for an accurate and diverse set of trained networks. Addemup works by first creating an initial population, then uses genetic operators to continually create new networks, keeping the set of networks that are as accurate as possible while disagreeing with each other as much as possible. Experiments on three DNA problems show that Addemup is able to generate a set of trained networks that is more accurate than several existing approaches. Experiments also show that Addemup is able to effectively incorporate prior knowledge, if available, to improve the quality of its ensemble. 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 #509379. 	[Title] Model selection using measure functions [Abstract] The concept of measure functions for generalization performance is suggested. This concept provides an alternative way of selecting and evaluating learned models (classifiers). In addition, it makes it possible to state a learning problem as a computational problem. The the known prior (meta-)knowledge about the problem domain is captured in a measure function that, to each possible combination of a training set and a classifier, assigns a value describing how good the classifier is. The computational problem is then to find a classifier maximizing the measure function. We argue that measure functions are of great value for practical applications. Besides of being a tool for model selection, they: (i) force us to make explicit the relevant prior knowledge about the learning problem at hand, (ii) provide a deeper understanding of existing algorithms, and (iii) help us in the construction of problem-specific algorithms. We illustrate the last point by suggesting a novel algorithm based on incremental search for a classifier that optimizes a given measure function. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4804. 	[Title] Toward Efficient Agnostic Learning [Abstract] In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed agnostic learning, in which we make virtually no assumptions on the target function. The name derives from the fact that as designers of learning algorithms, we give up the belief that Nature (as represented by the target function) has a simple or succinct explanation. We give a number of positive and negative results that provide an initial outline of the possibilities for agnostic learning. Our results include hardness results for the most obvious generalization of the PAC model to an agnostic setting, an efficient and general agnostic learning method based on dynamic programming, relationships between loss functions for agnostic learning, and an algorithm for a learning problem that involves hidden variables. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108551. 	[Title] From: Computational Learning Theory and Natural Systems, Chapter 18, "Cross-validation and Modal Theories", Cross-Validation and [Abstract] Cross-validation is a frequently used, intuitively pleasing technique for estimating the accuracy of theories learned by machine learning algorithms. During testing of a machine learning algorithm (foil) on new databases of prokaryotic RNA transcription promoters which we have developed, cross-validation displayed an interesting phenomenon. One theory is found repeatedly and is responsible for very little of the cross-validation error, whereas other theories are found very infrequently which tend to be responsible for the majority of the cross-validation error. It is tempting to believe that the most frequently found theory (the "modal theory") may be more accurate as a classifier of unseen data than the other theories. However, experiments showed that modal theories are not more accurate on unseen data than the other theories found less frequently during cross-validation. Modal theories may be useful in predicting when cross-validation is a poor estimate of true accuracy. We offer explanations 1 For correspondence: Department of Computer Science and Engineering, University of California, San Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #89547. 	[Title] Rigorous Learning Curve Bounds from Statistical Mechanics [Abstract] In this paper we introduce and investigate a mathematically rigorous theory of learning curves that is based on ideas from statistical mechanics. The advantage of our theory over the well-established Vapnik-Chervonenkis theory is that our bounds can be considerably tighter in many cases, and are also more reflective of the true behavior (functional form) of learning curves. This behavior can often exhibit dramatic properties such as phase transitions, as well as power law asymptotics not explained by the VC theory. The disadvantages of our theory are that its application requires knowledge of the input distribution, and it is limited so far to finite cardinality function classes. We illustrate our results with many concrete examples of learning curve bounds derived from our theory. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1997. 	[Title] Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule [Abstract] A training set of data has been used to construct a rule for predicting future responses. What is the error rate of this rule? The traditional answer to this question is given by cross-validation. The cross-validation estimate of prediction error is nearly unbiased, but can be highly variable. This article discusses bootstrap estimates of prediction error, which can be thought of as smoothed versions of cross-validation. A particular bootstrap method, the 632+ rule, is shown to substantially outperform cross-validation in a catalog of 24 simulation experiments. Besides providing point estimates, we also consider estimating the variability of an error rate estimate. All of the results here are nonparametric, and apply to any possible prediction rule: however we only study classification problems with 0-1 loss in detail. Our simulations include "smooth" prediction rules like Fisher's Linear Discriminant Function, and unsmooth ones like Nearest Neighbors. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #272720. 	[Title] A New Metric-Based Approach to Model Selection [Abstract] We introduce a new approach to model selection that performs better than the standard complexity-penalization and hold-out error estimation techniques in many cases. The basic idea is to exploit the intrinsic metric structure of a hypothesis space, as determined by the natural distribution of unlabeled training patterns, and use this metric as a reference to detect whether the empirical error estimates derived from a small (labeled) training sample can be trusted in the region around an empirically optimal hypothesis. Using simple metric intuitions we develop new geometric strategies for detecting overfitting and performing robust yet responsive model selection in spaces of candidate functions. These new metric-based strategies dramatically outperform previous approaches in experimental studies of classical polynomial curve fitting. Moreover, the technique is simple, efficient, and can be applied to most function learning tasks. The only requirement is access to an auxiliary collection of unlabeled training data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #463825. 	[Title] On the Induction of Intelligible Ensembles [Abstract] Ensembles of classifiers, e.g. decision trees, often exhibit greater predictive accuracy than single classifiers alone. Bagging and boosting are two standard ways of generating and combining multiple classifiers. Unfortunately, the increase in predictive performance is usually linked to a dramatic decrease in intelligibility: ensembles are more or less black boxes comparable to neural networks. So far attempts at pruning of ensembles have not been very successful, approximately reducing ensembles into half. This paper describes a different approach which both tries to keep ensemble-sizes small during induction already and also limits the complexity of single classifiers rigorously. Single classifiers are decision-stumps of a prespecified maximal depth. They are combined by majority voting. Ensembles are induced and pruned by a simple hill-climbing procedure. These ensembles can reasonably be transformed into equivalent decision trees. We conduct some empirical evaluation to investigate both predictive accuracies and classifier complexities. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #192850. 	[Title] Characterizing the generalization performance of model selection strategies [Abstract] We investigate the structure of model selection problems via the bias/variance decomposition. In particular, we characterize the essential aspects of a model selection task by the bias and variance profiles it generates over the sequence of hypothesis classes. With this view, we develop a new understanding of complexity-penalization methods: First, the penalty terms can be interpreted as postulating a particular profile for the variances as a function of model complexityif the postulated and true profiles do not match, then systematic under-fitting or over-fitting results, depending on whether the penalty terms are too large or too small. Second, we observe that it is generally best to penalize according to the true variances of the task, and therefore no fixed penalization strategy is optimal across all problems. We then use this characterization to introduce the notion of easy versus hard model selection problems. Here we show that if the variance profile grows too rapidly in relation to the biases, then standard model selection techniques become prone to significant errors. This can happen, for example, in regression problems where the independent variables are drawn from wide-tailed distributions. To counter this, we discuss a new model selection strategy that dramatically outperforms standard complexity-penalization and hold-out meth ods on these hard tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #601561. 	[Title] Applying a Machine Learning Workbench: Experience with Agricultural Databases [Abstract] This paper reviews our experience with the application of machine learning techniques to agricultural databases. We have designed and implemented a machine learning workbench, WEKA, which permits rapid experimentation on a given dataset using a variety of machine learning schemes, and has several facilities for interactive investigation of the data: preprocessing attributes, evaluating and comparing the results of different schemes, and designing comparative experiments to be run offline. We discuss the partnership between agricultural scientist and machine learning researcher that our experience has shown to be vital to success. We review in some detail a particular agricultural application concerned with the culling of dairy herds. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39130. 	[Title] Average-Case Analysis of a Nearest Neighbor Algorithm [Abstract] In this paper we present an average-case analysis of the nearest neighbor algorithm, a simple induction method that has been studied by many researchers. Our analysis assumes a conjunctive target concept, noise-free Boolean attributes, and a uniform distribution over the instance space. We calculate the probability that the algorithm will encounter a test instance that is distance d from the prototype of the concept, along with the probability that the nearest stored training case is distance e from this test instance. From this we compute the probability of correct classification as a function of the number of observed training cases, the number of relevant attributes, and the number of irrelevant attributes. We also explore the behavioral implications of the analysis by presenting predicted learning curves for artificial domains, and give experimental results on these domains as a check on our reasoning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1125467. 	[Title] Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting [Abstract] In a recent paper, Friedman, Geiger, and Goldszmidt [8] introduced a classifier based on Bayesian networks, called Tree Augmented Naive Bayes (TAN), that outperforms naive Bayes and performs competitively with C4.5 and other state-of-the-art methods. This classifier has several advantages including robustness and polynomial computational complexity. One limitation of the TAN classifier is that it applies only to discrete attributes, and thus, continuous attributes must be prediscretized. In this paper, we extend TAN to deal with continuous attributes directly via parametric (e.g., Gaussians) and semiparametric (e.g., mixture of Gaussians) conditional probabilities. The result is a classifier that can represent and combine both discrete and continuous attributes. In addition, we propose a new method that takes advantage of the modeling language of Bayesian networks in order to represent attributes both in discrete and continuous form simultaneously, and use both versions in the classification. This automates the process of deciding which form of the attribute is most relevant to the classification task. It also avoids the commitment to either a discretized or a (semi)parametric form, since different attributes may correlate better with one version or the other. Our empirical results show that this latter method usually achieves classification performance that is as good as or better than either the purely discrete or the purely continuous TAN models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #97377. 	[Title] Induction of One-Level Decision Trees [Abstract] In recent years, researchers have made considerable progress on the worst-case analysis of inductive learning tasks, but for theoretical results to have impact on practice, they must deal with the average case. In this paper we present an average-case analysis of a simple algorithm that induces one-level decision trees for concepts defined by a single relevant attribute. Given knowledge about the number of training instances, the number of irrelevant attributes, the amount of class and attribute noise, and the class and attribute distributions, we derive the expected classification accuracy over the entire instance space. We then examine the predictions of this analysis for different settings of these domain parameters, comparing them to exper imental results to check our reasoning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #154982. 	[Title] Bias, variance and prediction error for classification rules [Abstract] We study the notions of bias and variance for classification rules. Following Efron (1978) we develop a decomposition of prediction error into its natural components. Then we derive bootstrap estimates of these components and illustrate how they can be used to describe the error behaviour of a classifier in practice. In the process we also obtain a bootstrap estimate of the error of a "bagged" classifier. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #129897. 	[Title] The out-of-bootstrap method for model averaging and selection [Abstract] We propose a bootstrap-based method for model averaging and selection that focuses on training points that are left out of individual bootstrap samples. This information can be used to estimate optimal weighting factors for combining estimates from different bootstrap samples, and also for finding the best subsets the linear model setting. These proposals provide alternatives to Bayesian approaches to model averaging and selection, requiring less computation and fewer subjective choices. 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 #1102442. 	[Title] Unbiased Assessment of Learning Algorithms [Abstract] In order to rank the performance of machine learning algorithms, many researchers conduct experiments on benchmark data sets. Since most learning algorithms have domain-specific parameters, it is a popular custom to adapt these parameters to obtain a minimal error rate on the test set. The same rate is then used to rank the algorithm, which causes an optimistic bias. We quantify this bias, showing, in particular, that an algorithm with more parameters will probably be ranked higher than an equally good algorithm with fewer parameters. We demonstrate this result, showing the number of parameters and trials required in order to pretend to outperform C4.5 or FOIL, respectively, for various benchmark problems. We then describe out how unbiased ranking experiments should be conducted. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114222. 	[Title] Lazy Bayesian Trees [Abstract] The naive Bayesian classifier is simple and effective, but its attribute independence assumption is often violated in the real world. A number of approaches have been developed that seek to alleviate this problem. A Bayesian tree learning algorithm builds a decision tree, and generates a local Bayesian classifier at each leaf instead of predicting a single class. However, Bayesian tree learning still suffers from the replication and fragmentation problems of tree learning. While inferred Bayesian trees demonstrate low average prediction error rates, there is reason to believe that error rates will be higher for those leaves with few training examples. This paper proposes a novel lazy Bayesian tree learning algorithm. For each test example, it conceptually builds a most appropriate Bayesian tree. In practice, only one path with a local Bayesian classifier at its leaf is created. Experiments with a wide variety of real-world and artificial domains show that this new algorithm has significantly lower overall prediction error rates than a naive Bayesian classifier, C4.5, and a Bayesian tree learning algorithm. 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 #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 #7032. 	[Title] Combining estimates in regression and classification [Abstract] We consider the problem of how to combine a collection of general regression fit vectors in order to obtain a better predictive model. The individual fits may be from subset linear regression, ridge regression, or something more complex like a neural network. We develop a general framework for this problem and examine a recent cross-validation-based proposal called "stacking" in this context. Combination methods based on the bootstrap and analytic methods are also derived and compared in a number of examples, including best subsets regression and regression trees. Finally, we apply these ideas to classification problems where the estimated combination weights can yield insight into the structure of the problem. 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 #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 #3233 has the title `A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection  
` and the abstract `We review accuracy estimation methods and compare the two most common methods: cross-validation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leave-one-out cross-validation. We report on a large-scale experiment|over half a million runs of C4.5 and a Naive-Bayes algorithm|to estimate the effects of different parameters on these algorithms on real-world datasets. For cross-validation, we vary the number of folds and whether the folds are stratified or not; for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, the best method to use for model selection is ten-fold stratified cross validation, even if computation power allows using more folds. 
`.
Question: Which category should Paper #3233 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #3233 has the title `A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection  
` and the abstract `We review accuracy estimation methods and compare the two most common methods: cross-validation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leave-one-out cross-validation. We report on a large-scale experiment|over half a million runs of C4.5 and a Naive-Bayes algorithm|to estimate the effects of different parameters on these algorithms on real-world datasets. For cross-validation, we vary the number of folds and whether the folds are stratified or not; for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, the best method to use for model selection is ten-fold stratified cross validation, even if computation power allows using more folds. 
`.
Question: Which category should Paper #3233 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: Probabilistic_Methods