Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Machine Learning
Machine Learning
Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
Unsupervised Texture Segmentation in a Deterministic Annealing Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass learning, boosting, and error-correcting codes
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A global optimization technique for statistical classifier design
IEEE Transactions on Signal Processing
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
An incremental node embedding technique for error correcting output codes
Pattern Recognition
The effect of target vector selection on the invariance of classifier performance measures
IEEE Transactions on Neural Networks
Learning ECOC and dichotomizers jointly from data
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Thinned-ECOC ensemble based on sequential code shrinking
Expert Systems with Applications: An International Journal
Minimal design of error-correcting output codes
Pattern Recognition Letters
Error-correcting output codes based ensemble feature extraction
Pattern Recognition
On the design of an ECOC-Compliant Genetic Algorithm
Pattern Recognition
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The best-known decomposition schemes of multiclass learning problems are one per class coding (OPC) and error-correcting output coding (ECOC). Both methods perform a prior decomposition, that is, before training of the classifier takes place. The impact of output codes on the inferred decision rules can be experienced only after learning. Therefore, we present a novel algorithm for the code design of multiclass learning problems. This algorithm applies a maximum-likelihood objective function in conjunction with the expectation-maximization (EM) algorithm. Minimizing the augmented objective function yields the optimal decomposition of the multiclass learning problem in two-class problems. Experimental results show the potential gain of the optimized output codes over OPC or ECOC methods.