Catenary Support Vector Machines

Authors:
Kin Fai Kan;Christian R. Shelton
Affiliations:
Department of Computer Science and Engineering, University of California, Riverside, Riverside, USA CA 92521;Department of Computer Science and Engineering, University of California, Riverside, Riverside, USA CA 92521
Venue:
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Year:
2008

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Abstract

Many problems require making sequential decisions. For these problems, the benefit of acquiring further information must be weighed against the costs. In this paper, we describe the catenary support vector machine(catSVM), a margin-based method to solve sequential stopping problems. We provide theoretical guarantees for catSVM on future testing examples. We evaluated the performance of catSVM on UCI benchmark data and also applied it to the task of face detection. The experimental results show that catSVM can achieve a better cost tradeoff than single-stage SVM and chained boosting.