Localized projection learning
Kazuki Tsuji,Mineichi Kudo,Akira Tanaka +2 more
- 18 Aug 2010
- pp 90-99
TL;DR: It is shown that SVM is superior to projection learning in many classification problems in its optimal setting but the setting is not easy.
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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.
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