Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Localized Projection Learning
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 www.springerlink.com |
Type: | bookchapter (author version) |
URI: | http://hdl.handle.net/2115/48287 |
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
|