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Construction of convex hull classifiers in high dimensions

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Title: Construction of convex hull classifiers in high dimensions
Authors: Takahashi, Tetsuji Browse this author
Kudo, Mineichi Browse this author →KAKEN DB
Nakamura, Atsuyoshi Browse this author
Keywords: Pattern recognition
Convex hull
Classifier selection
Issue Date: 1-Dec-2011
Publisher: Elsevier B.V.
Journal Title: Pattern Recognition Letters
Volume: 32
Issue: 16
Start Page: 2224
End Page: 2230
Publisher DOI: 10.1016/j.patrec.2011.06.020
Abstract: We propose an algorithm to approximate each class region by a small number of approximated convex hulls and to use these for classification. The classifier is one of non-kernel maximum margin classifiers. It keeps the maximum margin in the original feature space, unlike support vector machines with a kernel. The construction of an exact convex hull requires an exponential time in dimension, so we find an approximate convex hull (a polyhedron) instead, which is constructed in linear time in dimension. We also propose a model selection procedure to control the number of faces of convex hulls for avoiding over-fitting, in which a fast procedure is adopted to calculate an upper-bound of the leave-one-out error. In comparison with support vector machines, the proposed approach is shown to be comparable in performance but more natural in the extension to multi-class problems.
Type: article (author version)
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 工藤 峰一

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