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Classification with non-i.i.d. sampling. (English) Zbl 1228.62074
Summary: We study learning algorithms for classification generated by regularization schemes in reproducing kernel Hilbert spaces associated with a general convex loss function in a non-i.i.d. process. Error analysis is studied and our main purpose is to provide elaborate capacity dependent error bounds by applying concentration techniques involving the $\ell ^{2}$-empirical covering numbers.

MSC:
 62H30 Classification and discrimination; cluster analysis (statistics) 68T99 Artificial intelligence 46N30 Applications of functional analysis in probability theory and statistics
Full Text:
References:
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