05372980

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05372980 Kan, Kin Fai Shelton, Christian R. Catenary support vector machines. Daelemans, Walter (ed.) et al., Machine learning and knowledge discovery in databases. European conference, ECML PKDD 2008, Antwerp, Belgium, September 15--19, 2008, Proceedings, Part I. Berlin: Springer (ISBN 978-3-540-87478-2/pbk). Lecture Notes in Computer Science 5211. Lecture Notes in Artificial Intelligence, 597-610 (2008). 2008 Berlin: Springer EN

doi:10.1007/978-3-540-87479-9_57

Summary: 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.