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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Localized Projection Learning
verfasst von : Kazuki Tsuji, Mineichi Kudo, Akira Tanaka
Erschienen in: Structural, Syntactic, and Statistical Pattern Recognition
Verlag: Springer Berlin Heidelberg
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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.