Catenary Support Vector Machines

被引：0

作者：

Kan, Kin Fai ^{[1
]}

Shelton, Christian R. ^{[1
]}

机构：

[1] Univ Calif Riverside, Dept Comp Sci & Engn, Riverside, CA 92521 USA

来源：

MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PART I, PROCEEDINGS | 2008年 / 5211卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 UCl benchmark data and also applied it to the task of face detection. The experiment results show that catSVM can achieve a better cost tradeoff than single-stage SVM and chained boosting.

引用

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页码：597 / 610

页数：14

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