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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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.

Paper #211906 has the title `Generalizations of the Bias/Variance Decomposition for Prediction Error  
` and the 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.
`.
Question: Which category should Paper #211906 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #211906 has the title `Generalizations of the Bias/Variance Decomposition for Prediction Error  
` and the 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.
`.
Question: Which category should Paper #211906 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