Support vector machines with beta-mixing input sequences

Authors:
Luoqing Li;Chenggao Wan
Affiliations:
Faculty of Mathematics and Computer Science, Hubei University, Wuhan, China;Faculty of Mathematics and Computer Science, Hubei University, Wuhan, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

This note mainly focuses on a theoretical analysis of support vector machines with beta-mixing input sequences. The explicit bounds are derived on the rate at which the empirical means converge to their true values when the underlying process is beta-mixing. The uniform convergence approach is used to estimate the convergence rates of the support vector machine algorithms with beta-mixing inputs.