Consistency of Support Vector Machines and other regularized kernel machines

by    Ingo Steinwart

Preprint series: 02-03, Reports on Analysis and Computer Science

The paper is published: Jenaer Schriften zur Mathematik und Informatik, Math/Inf/09/02

MSC:
68T10 Pattern recognition, speech recognition, {For cluster analysis, See 62H30}

Abstract: We show that various classifiers that are based on a minimization of a regularized risk are universally
consistent. In particular, several types of support vector machines as well as regularization networks are
treated. Our methods combine techniques from stochastic, approximation theory and functional analysis.


Keywords: Computational Learning Theory, Pattern Recognition, PAC Model, Support Vector Machines, Kernel Methods, Universal Consistency

Upload: 2002-06-20

Update: 2002-06-26


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