Stochastic organization of output codes in multiclass learning problems.

W Utschick; W Weichselberger

doi:10.1162/08997660151134334

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PubMed · 11359645

Stochastic organization of output codes in multiclass learning problems.

W Utschick·W Weichselberger

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Abstract

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.

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Algorithms Learning Likelihood Functions Models, Neurological Nerve Net Neural Networks, Computer Probability Stochastic Processes Journal Article

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BibTeX RIS

W Utschick, W Weichselberger. 2001. Stochastic organization of output codes in multiclass learning problems.. https://doi.org/10.1162/08997660151134334

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