Recent results in theoretical machine learning seem to suggest that nice properties of the margin distribution over a training set turns out in a good performance of a classifier. The same principle has been already used in SVM and other kernel based methods as the associated optimization problems try to maximize the minimum of these margins. In this paper, we propose a kernel based method for the direct optimization of the margin distribution (KM-OMD). The method is motivated and analyzed from a game theoretical perspective. A quite efficient optimization algorithm is then proposed. Experimental results over a standard benchmark of 13 datasets have clearly shown state-of-the-art performances.

A Kernel Method for the Optimization of the Margin Distribution

AIOLLI, FABIO;DA SAN MARTINO, GIOVANNI;SPERDUTI, ALESSANDRO
2008

Abstract

Recent results in theoretical machine learning seem to suggest that nice properties of the margin distribution over a training set turns out in a good performance of a classifier. The same principle has been already used in SVM and other kernel based methods as the associated optimization problems try to maximize the minimum of these margins. In this paper, we propose a kernel based method for the direct optimization of the margin distribution (KM-OMD). The method is motivated and analyzed from a game theoretical perspective. A quite efficient optimization algorithm is then proposed. Experimental results over a standard benchmark of 13 datasets have clearly shown state-of-the-art performances.
Proceedings of the International Conference on Neural Networks, ICANN 2008 , Part I, Lecture Notes in Computer Science
9783540875352
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/2436540
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