Communications of the ACM
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Proceedings of the third annual workshop on Computational learning theory
COLT90 3rd Annual Workshop on Computational Learning Theory
Proceedings of the second annual workshop on Computational learning theory
COLT'89 2nd Workshop on Computational Learning Theory
A comparative study of ID3 and backpropagation for English text-to-speech mapping
Proceedings of the seventh international conference (1990) on Machine learning
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Symbolic and Neural Learning Algorithms: An Experimental Comparison
Machine Learning
Machine Learning
Error Control Coding, Second Edition
Error Control Coding, Second Edition
Question classification with support vector machines and error correcting codes
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Using diversity measures for generating error-correcting output codes in classifier ensembles
Pattern Recognition Letters
Pattern Recognition Letters
An efficient way to learn English grapheme-to-phoneme rules automatically
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
ACACOS'11 Proceedings of the 10th WSEAS international conference on Applied computer and applied computational science
A comparison of random forest with ECOC-based classifiers
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Decoding of ternary error correcting output codes
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Some comments on error correcting output codes
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Robust multi-view face detection using error correcting output codes
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Hand geometry based recognition with a MLP classifier
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Decoding rules for error correcting output code ensembles
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Recursive ECOC for microarray data classification
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Tumor classification from gene expression data: a coding-based multiclass learning approach
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Personalization and user verification in wearable systems using biometric walking patterns
Personal and Ubiquitous Computing
A novel divide-and-merge classification for high dimensional datasets
Computational Biology and Chemistry
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
Inter-training: Exploiting unlabeled data in multi-classifier systems
Knowledge-Based Systems
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Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k 2 values (i.e., k "classes"). The definition is acquired by studying large collections of training examples of the form 〈Xi, f(Xi)〉. 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.