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Localized Projection Learning

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Title: Localized Projection Learning
Authors: Tsuji, Kazuki Browse this author
Kudo, Mineichi Browse this author →KAKEN DB
Tanaka, Akira Browse this author
Issue Date: 2010
Publisher: Springer Berlin / Heidelberg
Citation: Structural, Syntactic, and Statistical Pattern Recognition : Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings. Ed. by Edwin R. Hancock, Richard C. Wilson, Terry Windeatt, Ilkay Ulusoy, Francisco Escolano. ISBN: 978-3-642-14979-5. (Lecture Notes in Computer Science ; 6218) pp. 90-99.
Publisher DOI: 10.1007/978-3-642-14980-1_8
Abstract: It is interesting to compare different criteria of kernel machines. In this paper, the following is made: 1) To cope with the scaling problem of projection learning, we propose a dynamic localized projection learning using k nearest neighbors, 2) The localized method is compared with SVM from some viewpoints, and 3) Approximate nearest neighbors are demonstrated their usefulness in such a localization. As a result, it is shown that SVM is superior to projection learning in many classification problems in its optimal setting but the setting is not easy.
Rights: The original publication is available at
Type: bookchapter (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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