Gaps in support vector optimization

Authors:
Nikolas List;Don Hush;Clint Scovel;Ingo Steinwart
Affiliations:
Lehrstuhl Mathematik und Informatik, Ruhr-University Bochum, Germany;CCS-3, Informatics Group, Los Alamos National Laboratory, Los Alamos, New Mexico;CCS-3, Informatics Group, Los Alamos National Laboratory, Los Alamos, New Mexico;CCS-3, Informatics Group, Los Alamos National Laboratory, Los Alamos, New Mexico
Venue:
COLT'07 Proceedings of the 20th annual conference on Learning theory
Year:
2007

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Abstract

We show that the stopping criteria used in many support vector machine (SVM) algorithms working on the dual can be interpreted as primal optimality bounds which in turn are known to be important for the statistical analysis of SVMs. To this end we revisit the duality theory underlying the derivation of the dual and show that in many interesting cases primal optimality bounds are the same as known dual optimality bounds.