Above are papers related to the following paper: [Paper_ID] Paper #27250. 	[Title] Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a [Abstract] The error rate of decision-tree and other classification learners can often be much reduced by bagging: learning multiple models from bootstrap samples of the database, and combining them by uniform voting. In this paper we empirically test two alternative explanations for this, both based on Bayesian learning theory: (1) bagging works because it is an approximation to the optimal procedure of Bayesian model averaging, with an appropriate implicit prior; (2) bagging works because it effectively shifts the prior to a more appropriate region of model space. All the experimental evidence contradicts the first hypothesis, and confirms the second. Bagging (Breiman 1996a) is a simple and effective way to reduce the error rate of many classification learning algorithms. For example, in the empirical study described below, it reduces the error of a decision-tree learner in 19 of 26 databases, by 4% on average. In the bagging procedure, given a training set of size s, a "bootstrap" replicate of it is constructed by taking s samples with replacement from the training set. Thus a new training set of the same size is produced, where each of the original examples may appear once, more than once, or not. On average, 63% of the original examples will appear in the bootstrap sample. The learning algorithm is then applied to this training set. This procedure is repeated m times, and the resulting m models are aggregated by uniform voting. Bagging is one of several "multiple model" approaches that have recently received much attention (see, for example, (Chan, Stolfo, & Wolpert 1996)). Other procedures of this type include boosting (Freund & Schapire 1996) and stacking (Wolpert 1992). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131266. 	[Title] An Improved Model for Spatially Correlated Binary Responses [Abstract] In this paper we extend the basic autologistic model to include covariates and an indication of sampling effort. The model is applied to sampled data instead of the traditional use for image analysis where complete data are available. We adopt a Bayesian set-up and develop a hybrid Gibbs sampling estimation procedure. Using simulated examples, we show that the autologistic model with covariates for sample data improves predictions as compared to the simple logistic regression model and the standard autologistic model (without covariates). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #189574. 	[Title] Theory Refinement on Bayesian Networks [Abstract] Theory refinement is the task of updating a domain theory in the light of new cases, to be done automatically or with some expert assistance. The problem of theory refinement under uncertainty is reviewed here in the context of Bayesian statistics, a theory of belief revision. The problem is reduced to an incremental learning task as follows: the learning system is initially primed with a partial theory supplied by a domain expert, and thereafter maintains its own internal representation of alternative theories which is able to be interrogated by the domain expert and able to be incrementally refined from data. Algorithms for refinement of Bayesian networks are presented to illustrate what is meant by "partial theory", "alternative theory representation", etc. The algorithms are an incremental variant of batch learning algorithms from the literature so can work well in batch and incremental mode. 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 #1107861. 	[Title] Boosting Trees for Cost-Sensitive Classifications [Abstract] This paper explores two boosting techniques for cost-sensitive tree classification in the situation where misclassification costs change very often. Ideally, one would like to have only one induction, and use the induced model for different misclassification costs. Thus, it demands robustness of the induced model against cost changes. Combining multiple trees gives robust predictions against this change. We demonstrate that ordinary boosting combined with the minimum expected cost criterion to select the prediction class is a good solution under this situation. We also introduce a variant of the ordinary boosting procedure which utilizes the cost information during training. We show that the proposed technique performs better than the ordinary boosting in terms of misclassification cost. However, this technique requires to induce a set of new trees every time the cost changes. Our empirical investigation also reveals some interesting behavior of boosting decision trees for cost-sensitive classification. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #197783. 	[Title] Adaptive Boosting of Neural Networks for Character Recognition [Abstract] Technical Report #1072, D epartement d'Informatique et Recherche Op erationnelle, Universit e de Montr eal Abstract Boosting is a general method for improving the performance of any learning algorithm that consistently generates classifiers which need to perform only slightly better than random guessing. A recently proposed and very promising boosting algorithm is AdaBoost [5]. It has been applied with great success to several benchmark machine learning problems using rather simple learning algorithms [4], in particular decision trees [1, 2, 6]. In this paper we use AdaBoost to improve the performances of neural networks applied to character recognition tasks. We compare training methods based on sampling the training set and weighting the cost function. Our system achieves about 1.4% error on a data base of online handwritten digits from more than 200 writers. Adaptive boosting of a multi-layer network achieved 2% error on the UCI Letters offline characters data set. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #101662. 	[Title] Learning from positive data [Abstract] Gold showed in 1967 that not even regular grammars can be exactly identified from positive examples alone. Since it is known that children learn natural grammars almost exclusively from positives examples, Gold's result has been used as a theoretical support for Chomsky's theory of innate human linguistic abilities. In this paper new results are presented which show that within a Bayesian framework not only grammars, but also logic programs are learnable with arbitrarily low expected error from positive examples only. In addition, we show that the upper bound for expected error of a learner which maximises the Bayes' posterior probability when learning from positive examples is within a small additive term of one which does the same from a mixture of positive and negative examples. An Inductive Logic Programming implementation is described which avoids the pitfalls of greedy search by global optimisation of this function during the local construction of individual clauses of the hypothesis. Results of testing this implementation on artificially-generated data-sets are reported. These results are in agreement with the theoretical predictions. 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 #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 #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 #1153064. 	[Title] Stochastic Logic Programs [Abstract] One way to represent a machine learning algorithm's bias over the hypothesis and instance space is as a pair of probability distributions. This approach has been taken both within Bayesian learning schemes and the framework of U-learnability. However, it is not obvious how an Inductive Logic Programming (ILP) system should best be provided with a probability distribution. This paper extends the results of a previous paper by the author which introduced stochastic logic programs as a means of providing a structured definition of such a probability distribution. Stochastic logic programs are a generalisation of stochastic grammars. A stochastic logic program consists of a set of labelled clauses p : C where p is from the interval [0; 1] and C is a range-restricted definite clause. A stochastic logic program P has a distributional semantics, that is one which assigns a probability distribution to the atoms of each predicate in the Herbrand base of the clauses in P . These probabilities are assigned to atoms according to an SLD-resolution strategy which employs a stochastic selection rule. It is shown that the probabilities can be computed directly for fail-free logic programs and by normalisation for arbitrary logic programs. The stochastic proof strategy can be used to provide three distinct functions: 1) a method of sampling from the Herbrand base which can be used to provide selected targets or example sets for ILP experiments, 2) a measure of the information content of examples or hypotheses; this can be used to guide the search in an ILP system and 3) a simple method for conditioning a given stochastic logic program on samples of data. Functions 1) and 3) are used to measure the generality of hypotheses in the ILP system Progol4.2. This supports an implementation of a Bayesian technique for learning from positive examples only. fl This paper is an extension of a paper with the same title which appeared in [12] Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1116842. 	[Title] Pruning Adaptive Boosting ICML-97 Final Draft [Abstract] The boosting algorithm AdaBoost, developed by Freund and Schapire, has exhibited outstanding performance on several benchmark problems when using C4.5 as the "weak" algorithm to be "boosted." Like other ensemble learning approaches, AdaBoost constructs a composite hypothesis by voting many individual hypotheses. In practice, the large amount of memory required to store these hypotheses can make ensemble methods hard to deploy in applications. This paper shows that by selecting a subset of the hypotheses, it is possible to obtain nearly the same levels of performance as the entire set. The results also provide some insight into the behavior of AdaBoost. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1118764. 	[Title] An Efficient Extension to Mixture Techniques for Prediction and Decision Trees [Abstract] We present a method for maintaining mixtures of prunings of a prediction or decision tree that extends the "node-based" prunings of [Bun90, WST95, HS97] to the larger class of edge-based prunings. The method includes an efficient online weight allocation algorithm that can be used for prediction, compression and classification. Although the set of edge-based prunings of a given tree is much larger than that of node-based prunings, our algorithm has similar space and time complexity to that of previous mixture algorithms for trees. Using the general on-line framework of Freund and Schapire [FS97], we prove that our algorithm maintains correctly the mixture weights for edge-based prunings with any bounded loss function. We also give a similar algorithm for the logarithmic loss function with a corresponding weight allocation algorithm. Finally, we describe experiments comparing node-based and edge-based mixture models for estimating the probability of the next word in English text, which show the ad vantages of edge-based models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #52784. 	[Title] An Empirical Evaluation of Bagging and Boosting [Abstract] An ensemble consists of a set of independently trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman 1996a) and Boosting (Freund & Schapire 1996) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods using both neural networks and decision trees as our classification algorithms. Our results clearly show two important facts. The first is that even though Bagging almost always produces a better classifier than any of its individual component classifiers and is relatively impervious to overfitting, it does not generalize any better than a baseline neural-network ensemble method. The second is that Boosting is a powerful technique that can usually produce better ensembles than Bagging; however, it is more susceptible to noise and can quickly overfit a data set. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #101811. 	[Title] Programming Research Group A LEARNABILITY MODEL FOR UNIVERSAL REPRESENTATIONS [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #84020. 	[Title] Classifiers: A Theoretical and Empirical Study [Abstract] This paper describes how a competitive tree learning algorithm can be derived from first principles. The algorithm approximates the Bayesian decision theoretic solution to the learning task. Comparative experiments with the algorithm and the several mature AI and statistical families of tree learning algorithms currently in use show the derived Bayesian algorithm is consistently as good or better, although sometimes at computational cost. Using the same strategy, we can design algorithms for many other supervised and model learning tasks given just a probabilistic representation for the kind of knowledge to be learned. As an illustration, a second learning algorithm is derived for learning Bayesian networks from data. Implications to incremental learning and the use of multiple models are also discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #73146. 	[Title] LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE [Abstract] We examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local structure in the conditional probability distributions (CPDs) that quantify these networks. This increases the space of possible models, enabling the representation of CPDs with a variable number of parameters. The resulting learning procedure induces models that better emulate the interactions present in the data. We describe the theoretical foundations and practical aspects of learning local structures and provide an empirical evaluation of the proposed learning procedure. This evaluation indicates that learning curves characterizing this procedure converge faster, in the number of training instances, than those of the standard procedure, which ignores the local structure of the CPDs. Our results also show that networks learned with local structures tend to be more complex (in terms of arcs), yet require fewer parameters. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #23738. 	[Title] A THEORY OF LEARNING CLASSIFICATION RULES [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #11325. 	[Title] On Learning Multiple Descriptions of a Concept [Abstract] In sparse data environments, greater classification accuracy can be achieved by learning several concept descriptions of the data and combining their classifications. Stochastic search is a general tool which can be used to generate many good concept descriptions (rule sets) for each class in the data. Bayesian probability theory offers an optimal strategy for combining classifications of the individual concept descriptions, and here we use an approximation of that theory. This strategy is most useful when additional data is difficult to obtain and every increase in classification accuracy is important. The primary result of this paper is that multiple concept descriptions are particularly helpful in "flat" hypothesis spaces in which there are many equally good ways to grow a rule, each having similar gain. Another result is experimental evidence that learning multiple rule sets yields more accurate classifications than learning multiple rules for some domains. To demonstrate these behaviors, we learn multiple concept descriptions by adapting HYDRA, a noise-tolerant relational learning algorithm. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #211906. 	[Title] Generalizations of the Bias/Variance Decomposition for Prediction Error [Abstract] The bias and variance of a real valued random variable, using squared error loss, are well understood. However because of recent developments in classification techniques it has become desirable to extend these concepts to general random variables and loss functions. The 0-1 (misclassification) loss function with categorical random variables has been of particular interest. We explore the concepts of variance and bias and develop a decomposition of the prediction error into functions of the systematic and variable parts of our predictor. After providing some examples we conclude with a discussion of the various definitions that have been proposed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106630. 	[Title] Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks [Abstract] Neural networks have been successfully applied in a wide range of supervised and unsupervised learning applications. Neural-network methods are not commonly used for data-mining tasks, however, because they often produce incomprehensible models and require long training times. In this article, we describe neural-network learning algorithms that are able to produce comprehensible models, and that do not require excessive training times. Specifically, we discuss two classes of approaches for data mining with neural networks. The first type of approach, often called rule extraction, involves extracting symbolic models from trained neural networks. The second approach is to directly learn simple, easy-to-understand networks. We argue that, given the current state of the art, neural-network methods deserve a place in the tool boxes of data-mining specialists. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152075. 	[Title]  [Abstract] ARCING THE EDGE Leo Breiman Technical Report 486 , Statistics Department University of California, Berkeley CA. 94720 Abstract Recent work has shown that adaptively reweighting the training set, growing a classifier using the new weights, and combining the classifiers constructed to date can significantly decrease generalization error. Procedures of this type were called arcing by Breiman[1996]. The first successful arcing procedure was introduced by Freund and Schapire[1995,1996] and called Adaboost. In an effort to explain why Adaboost works, Schapire et.al. [1997] derived a bound on the generalization error of a convex combination of classifiers in terms of the margin. We introduce a function called the edge, which differs from the margin only if there are more than two classes. A framework for understanding arcing algorithms is defined. In this framework, we see that the arcing algorithms currently in the literature are optimization algorithms which minimize some function of the edge. A relation is derived between the optimal reduction in the maximum value of the edge and the PAC concept of weak learner. Two algorithms are described which achieve the optimal reduction. Tests on both synthetic and real data cast doubt on the Schapire et.al. There is recent empirical evidence that significant reductions in generalization error can be gotten by growing a number of different classifiers on the same training set and letting these vote for the best class. Freund and Schapire ([1995], [1996] ) proposed an algorithm called AdaBoost which adaptively reweights the training set in a way based on the past history of misclassifications, constructs a new classifier using the current weights, and uses the misclassification rate of this classifier to determine the size of its vote. In a number of empirical studies on many data sets using trees (CART or C4.5) as the base classifier (Drucker and Cortes[1995], Quinlan[1996], Freud and Schapire[1996], Breiman[1996]) AdaBoost produced dramatic decreases in generalization error compared to using a single tree. Error rates were reduced to the point where tests on some well-known data sets gave the result that CART plus AdaBoost did significantly better than any other of the commonly used classification methods (Breiman[1996] ). Meanwhile, empirical results showed that other methods of adaptive resampling (or reweighting) and combining (called "arcing" by Breiman [1996]) also led to low test set error rates. An algorithm called arc-x4 (Breiman[1996]) gave error rates almost identical to Adaboost. Ji and Ma[1997] worked with classifiers consisting of randomly selected hyperplanes and using a different method of adaptive resampling and unweighted voting, also got low error rates. Thus, there are a least three arcing algorithms extant, all of which give excellent classification accuracy. explanation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1121867. 	[Title] MAJORITY VOTE CLASSIFIERS: THEORY AND APPLICATIONS [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #11342. 	[Title] On Pruning and Averaging Decision Trees [Abstract] Pruning a decision tree is considered by some researchers to be the most important part of tree building in noisy domains. While, there are many approaches to pruning, an alternative approach of averaging over decision trees has not received as much attention. We perform an empirical comparison of pruning with the approach of averaging over decision trees. For this comparison we use a computa-tionally efficient method of averaging, namely averaging over the extended fanned set of a tree. Since there are a wide range of approaches to pruning, we compare tree averaging with a traditional pruning approach, along with an optimal pruning approach. 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 #1104851. 	[Title] Improving Bagging Performance by Increasing Decision Tree Diversity [Abstract] Ensembles of decision trees often exhibit greater predictive accuracy than single trees alone. Bagging and boosting are two standard ways of generating and combining multiple trees. Boosting has been empirically determined to be the more effective of the two, and it has recently been proposed that this may be because it produces more diverse trees than bagging. This paper reports empirical findings that strongly support this hypothesis. We enforce greater decision tree diversity in bagging by a simple modification of the underlying decision tree learner that utilizes randomly-generated decision stumps of predefined depth as the starting point for tree induction. The modified procedure yields very competitive results while still retaining one of the attractive properties of bagging: all iterations are independent. Additionally, we also investigate a possible integration of bagging and boosting. All these ensemble-generating procedures are compared empirically on various domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #411092. 	[Title] LEARNING LOGICAL EXCEPTIONS IN CHESS [Abstract]  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 #1123926. 	[Title] Achieving High-Accuracy Text-to-Speech with Machine Learning [Abstract] In 1987, Sejnowski and Rosenberg developed their famous NETtalk system for English text-to-speech. This chapter describes a machine learning approach to text-to-speech that builds upon and extends the initial NETtalk work. Among the many extensions to the NETtalk system were the following: a different learning algorithm, a wider input "window", error-correcting output coding, a right-to-left scan of the word to be pronounced (with the results of each decision influencing subsequent decisions), and the addition of several useful input features. These changes yielded a system that performs much better than the original NETtalk system. After training on 19,002 words, the system achieves 93.7% correct pronunciation of individual phonemes and 64.8% correct pronunciation of whole words (where the pronunciation must exactly match the dictionary pronunciation to be correct). Based on the judgements of three human participants in a blind assessment study, our system was estimated to have a serious error rate of 16.7% (on whole words) compared to an error rate of 26.1% for the DECTalk3.0 rulebase. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2654. 	[Title] Experiments with a New Boosting Algorithm [Abstract] In an earlier paper, we introduced a new boosting algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the related notion of a pseudo-loss which is a method for forcing a learning algorithm of multi-label concepts to concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems. We performed two sets of experiments. The first set compared boosting to Breiman's bagging method when used to aggregate various classifiers (including decision trees and single attribute-value tests). We compared the performance of the two methods on a collection of machine-learning benchmarks. In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #105057. 	[Title] Error-Correcting Output Codes for Local Learners [Abstract] Error-correcting output codes (ECOCs) represent classes with a set of output bits, where each bit encodes a binary classification task corresponding to a unique partition of the classes. Algorithms that use ECOCs learn the function corresponding to each bit, and combine them to generate class predictions. ECOCs can reduce both variance and bias errors for multiclass classification tasks when the errors made at the output bits are not correlated. They work well with algorithms that eagerly induce global classifiers (e.g., C4.5) but do not assist simple local classifiers (e.g., nearest neighbor), which yield correlated predictions across the output bits. We show that the output bit predictions of local learners can be decorrelated by selecting different features for each bit. We present promising empirical results for this combination of ECOCs, near est neighbor, and feature selection. 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 #45052. 	[Title] Bayesian Model Averaging [Abstract] Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident inferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA) provides a coherent mechanism for accounting for this model uncertainty. Several methods for implementing BMA have recently emerged. We discuss these methods and present a number of examples. In these examples, BMA provides improved out-of-sample predictive performance. We also provide a catalogue of currently available BMA software. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27249. 	[Title] Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms [Abstract] The term "bias" is widely used|and with different meanings|in the fields of machine learning and statistics. This paper clarifies the uses of this term and shows how to measure and visualize the statistical bias and variance of learning algorithms. Statistical bias and variance can be applied to diagnose problems with machine learning bias, and the paper shows four examples of this. Finally, the paper discusses methods of reducing bias and variance. Methods based on voting can reduce variance, and the paper compares Breiman's bagging method and our own tree randomization method for voting decision trees. Both methods uniformly improve performance on data sets from the Irvine repository. Tree randomization yields perfect performance on the Letter Recognition task. A weighted nearest neighbor algorithm based on the infinite bootstrap is also introduced. In general, decision tree algorithms have moderate-to-high variance, so an important implication of this work is that variance|rather than appropriate or inappropriate machine learning bias|is an important cause of poor performance for decision tree algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #65650. 	[Title] Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree [Abstract] Many algorithms for inferring a decision tree from data involve a two-phase process: First, a very large decision tree is grown which typically ends up "over-fitting" the data. To reduce over-fitting, in the second phase, the tree is pruned using one of a number of available methods. The final tree is then output and used for classification on test data. In this paper, we suggest an alternative approach to the pruning phase. Using a given unpruned decision tree, we present a new method of making predictions on test data, and we prove that our algorithm's performance will not be "much worse" (in a precise technical sense) than the predictions made by the best reasonably small pruning of the given decision tree. Thus, our procedure is guaranteed to be competitive (in terms of the quality of its predictions) with any pruning algorithm. We prove that our procedure is very efficient and highly robust. Our method can be viewed as a synthesis of two previously studied techniques. First, we apply Cesa-Bianchi et al.'s [4] results on predicting using "expert advice" (where we view each pruning as an "expert") to obtain an algorithm that has provably low prediction loss, but that is com-putationally infeasible. Next, we generalize and apply a method developed by Buntine [3], [2] and Willems, Shtarkov and Tjalkens [20], [21] to derive a very efficient implementation of this procedure. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27895. 	[Title] Bibliography "SMART: Support Management Automated Reasoning Technology for COMPAQ Customer Service," "Instance-Based Learning Algorithms," Machine [Abstract]  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 #1153148. 	[Title] Learning Classification Trees [Abstract] Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statistics. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule is similar to Quinlan's information gain, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 (Quinlan et al., 1987) and Breiman et al.'s CART (Breiman et al., 1984) show the full Bayesian algorithm can produce Publication: This paper is a final draft submitted for publication to the Statistics and Computing journal; a version with some minor changes appeared in Volume 2, 1992, pages 63-73. more accurate predictions than versions of these other approaches, though pay a computational price. Please summarise all the given information.

Paper #27250 has the title `Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a
` and the abstract `The error rate of decision-tree and other classification learners can often be much reduced by bagging: learning multiple models from bootstrap samples of the database, and combining them by uniform voting. In this paper we empirically test two alternative explanations for this, both based on Bayesian learning theory: (1) bagging works because it is an approximation to the optimal procedure of Bayesian model averaging, with an appropriate implicit prior; (2) bagging works because it effectively shifts the prior to a more appropriate region of model space. All the experimental evidence contradicts the first hypothesis, and confirms the second. Bagging (Breiman 1996a) is a simple and effective way to reduce the error rate of many classification learning algorithms. For example, in the empirical study described below, it reduces the error of a decision-tree learner in 19 of 26 databases, by 4% on average. In the bagging procedure, given a training set of size s, a "bootstrap" replicate of it is constructed by taking s samples with replacement from the training set. Thus a new training set of the same size is produced, where each of the original examples may appear once, more than once, or not. On average, 63% of the original examples will appear in the bootstrap sample. The learning algorithm is then applied to this training set. This procedure is repeated m times, and the resulting m models are aggregated by uniform voting. Bagging is one of several "multiple model" approaches that have recently received much attention (see, for example, (Chan, Stolfo, & Wolpert 1996)). Other procedures of this type include boosting (Freund & Schapire 1996) and stacking (Wolpert 1992). 
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Question: Which category should Paper #27250 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #27250 has the title `Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a
` and the abstract `The error rate of decision-tree and other classification learners can often be much reduced by bagging: learning multiple models from bootstrap samples of the database, and combining them by uniform voting. In this paper we empirically test two alternative explanations for this, both based on Bayesian learning theory: (1) bagging works because it is an approximation to the optimal procedure of Bayesian model averaging, with an appropriate implicit prior; (2) bagging works because it effectively shifts the prior to a more appropriate region of model space. All the experimental evidence contradicts the first hypothesis, and confirms the second. Bagging (Breiman 1996a) is a simple and effective way to reduce the error rate of many classification learning algorithms. For example, in the empirical study described below, it reduces the error of a decision-tree learner in 19 of 26 databases, by 4% on average. In the bagging procedure, given a training set of size s, a "bootstrap" replicate of it is constructed by taking s samples with replacement from the training set. Thus a new training set of the same size is produced, where each of the original examples may appear once, more than once, or not. On average, 63% of the original examples will appear in the bootstrap sample. The learning algorithm is then applied to this training set. This procedure is repeated m times, and the resulting m models are aggregated by uniform voting. Bagging is one of several "multiple model" approaches that have recently received much attention (see, for example, (Chan, Stolfo, & Wolpert 1996)). Other procedures of this type include boosting (Freund & Schapire 1996) and stacking (Wolpert 1992). 
`.
Question: Which category should Paper #27250 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