New results on error correcting output codes of kernel machines

IEEE Transactions on Neural Networks
Andrea PasseriniPaolo Frasconi

Abstract

We study the problem of multiclass classification within the framework of error correcting output codes (ECOC) using margin-based binary classifiers. Specifically, we address two important open problems in this context: decoding and model selection. The decoding problem concerns how to map the outputs of the classifiers into class codewords. In this paper we introduce a new decoding function that combines the margins through an estimate of their class conditional probabilities. Concerning model selection, we present new theoretical results bounding the leave-one-out (LOO) error of ECOC of kernel machines, which can be used to tune kernel hyperparameters. We report experiments using support vector machines as the base binary classifiers, showing the advantage of the proposed decoding function over other functions of the margin commonly used in practice. Moreover, our empirical evaluations on model selection indicate that the bound leads to good estimates of kernel parameters.

References

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Citations

Apr 12, 2011·Journal of Medical Systems·Raúl Ramos-PollánEugénio Oliveira
Sep 24, 2004·BMC Bioinformatics·Gustavo Camps-VallsErik L L Sonnhammer
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Anderson Rocha, Siome Klein Goldenstein
May 27, 2006·IEEE Transactions on Pattern Analysis and Machine Intelligence·Oriol PujolJordi Vitrià
May 26, 2006·IEEE Transactions on Neural Networks·Ben Fei, Jinbai Liu
Nov 22, 2008·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Lei WangKap Luk Chan
Nov 21, 2009·IEEE Transactions on Pattern Analysis and Machine Intelligence·Sergio EscaleraPetia Radeva
Oct 4, 2011·IEEE Transactions on Neural Networks·Feiping NieChangshui Zhang

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