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R-LAIR: Riverside Lab for Artificial Intelligence Research

Catenary Support Vector Machines (2008)

by Kin Fai Kan and Christian R. Shelton

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.

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Kin Fai Kan and Christian R. Shelton (2008). "Catenary Support Vector Machines." Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (LNAI, vol 5211) (pp. 597-610). © Springer-Verlag. pdf      

Bibtex citation

@inproceedings{KanShe08b,
   author = "Kin Fai Kan and Christian R. Shelton",
   title = "Catenary Support Vector Machines",
   booktitle = "Machine Learning and Knowledge Discovery in Databases (ECML/PKDD)",
   booktitleabbr = "ECML/PKDD",
   series = "LNAI",
   volume = 5211,
   year = 2008,
   pages = "597--610",
}

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