Above are papers related to the following paper: [Paper_ID] Paper #350373. 	[Title] Probability Estimation via Error-Correcting Output Coding [Abstract] Previous research has shown that a technique called error-correcting output coding (ECOC) can dramatically improve the classification accuracy of supervised learning algorithms that learn to classify data points into one of k 2 classes. In this paper, we will extend the technique so that ECOC can also provide class probability information. ECOC is a method of converting k-class supervised learning problem into a large number L of two-class supervised learning problems and then combining the results of these L evaluations. The underlying two-class supervised learning algorithms are assumed to provide L probability estimates. The problem of computing class probabilities is formulated as an over-constrained system of L linear equations. Least squares methods are applied to solve these equations. Accuracy and reliability of the probability estimates are demonstrated. 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 #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 #46500. 	[Title] Learning Complex Boolean Functions: Algorithms and Applications [Abstract] The most commonly used neural network models are not well suited to direct digital implementations because each node needs to perform a large number of operations between floating point values. Fortunately, the ability to learn from examples and to generalize is not restricted to networks of this type. Indeed, networks where each node implements a simple Boolean function (Boolean networks) can be designed in such a way as to exhibit similar properties. Two algorithms that generate Boolean networks from examples are presented. The results show that these algorithms generalize very well in a class of problems that accept compact Boolean network descriptions. The techniques described are general and can be applied to tasks that are not known to have that characteristic. Two examples of applications are presented: image reconstruction and hand-written character recognition. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #57764. 	[Title] Inductive Learning by Selection of Minimal Complexity Representations [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63915. 	[Title] Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models [Abstract] Learning the past tense of English verbs | a seemingly minor aspect of language acquisition | has generated heated debates since 1986, and has become a landmark task for testing the adequacy of cognitive modeling. Several artificial neural networks (ANNs) have been implemented, and a challenge for better symbolic models has been posed. In this paper, we present a general-purpose Symbolic Pattern Associator (SPA) based upon the decision-tree learning algorithm ID3. We conduct extensive head-to-head comparisons on the generalization ability between ANN models and the SPA under different representations. We conclude that the SPA generalizes the past tense of unseen verbs better than ANN models by a wide margin, and we offer insights as to why this should be the case. We also discuss a new default strategy for decision-tree learning algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #36140. 	[Title] Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs [Abstract] Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k &gt; 2 values (i.e., k "classes"). The definition is acquired by studying large collections of training examples of the form hx i ; f(x i )i. Existing approaches to this problem include (a) direct application of multiclass algorithms such as the decision-tree algorithms ID3 and CART, (b) application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and (c) application of binary concept learning algorithms with distributed output codes such as those employed by Sejnowski and Rosenberg in the NETtalk system. This paper compares these three approaches to a new technique in which BCH error-correcting codes are employed as a distributed output representation. We show that these output representations improve the performance of ID3 on the NETtalk task and of backpropagation on an isolated-letter speech-recognition task. These results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems. 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 #35797. 	[Title] Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules [Abstract] Proben1 is a collection of problems for neural network learning in the realm of pattern classification and function approximation plus a set of rules and conventions for carrying out benchmark tests with these or similar problems. Proben1 contains 15 data sets from 12 different domains. All datasets represent realistic problems which could be called diagnosis tasks and all but one consist of real world data. The datasets are all presented in the same simple format, using an attribute representation that can directly be used for neural network training. Along with the datasets, Proben1 defines a set of rules for how to conduct and how to document neural network benchmarking. The purpose of the problem and rule collection is to give researchers easy access to data for the evaluation of their algorithms and networks and to make direct comparison of the published results feasible. This report describes the datasets and the benchmarking rules. It also gives some basic performance measures indicating the difficulty of the various problems. These measures can be used as baselines for comparison. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12439. 	[Title] Improving the Performance of Radial Basis Function Networks by Learning Center Locations [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #323128. 	[Title] Error-Correcting Output Coding Corrects Bias and Variance [Abstract] Previous research has shown that a technique called error-correcting output coding (ECOC) can dramatically improve the classification accuracy of supervised learning algorithms that learn to classify data points into one of k 2 classes. This paper presents an investigation of why the ECOC technique works, particularly when employed with decision-tree learning algorithms. It shows that the ECOC method| like any form of voting or committee|can reduce the variance of the learning algorithm. Furthermore|unlike methods that simply combine multiple runs of the same learning algorithm|ECOC can correct for errors caused by the bias of the learning algorithm. Experiments show that this bias correction ability relies on the non-local be havior of C4.5. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1104055. 	[Title] LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #112378. 	[Title] Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion [Abstract] We describe an approach to grapheme-to-phoneme conversion which is both language-independent and data-oriented. Given a set of examples (spelling words with their associated phonetic representation) in a language, a grapheme-to-phoneme conversion system is automatically produced for that language which takes as its input the spelling of words, and produces as its output the phonetic transcription according to the rules implicit in the training data. We describe the design of the system, and compare its performance to knowledge-based and alternative data-oriented approaches. Please summarise all the given information.

Paper #350373 has the title `Probability Estimation via Error-Correcting Output Coding  
` and the abstract `Previous research has shown that a technique called error-correcting output coding (ECOC) can dramatically improve the classification accuracy of supervised learning algorithms that learn to classify data points into one of k 2 classes. In this paper, we will extend the technique so that ECOC can also provide class probability information. ECOC is a method of converting k-class supervised learning problem into a large number L of two-class supervised learning problems and then combining the results of these L evaluations. The underlying two-class supervised learning algorithms are assumed to provide L probability estimates. The problem of computing class probabilities is formulated as an over-constrained system of L linear equations. Least squares methods are applied to solve these equations. Accuracy and reliability of the probability estimates are demonstrated.
`.
Question: Which category should Paper #350373 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #350373 has the title `Probability Estimation via Error-Correcting Output Coding  
` and the abstract `Previous research has shown that a technique called error-correcting output coding (ECOC) can dramatically improve the classification accuracy of supervised learning algorithms that learn to classify data points into one of k 2 classes. In this paper, we will extend the technique so that ECOC can also provide class probability information. ECOC is a method of converting k-class supervised learning problem into a large number L of two-class supervised learning problems and then combining the results of these L evaluations. The underlying two-class supervised learning algorithms are assumed to provide L probability estimates. The problem of computing class probabilities is formulated as an over-constrained system of L linear equations. Least squares methods are applied to solve these equations. Accuracy and reliability of the probability estimates are demonstrated.
`.
Question: Which category should Paper #350373 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