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. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Tsuji, K., Kudo, M., & Tanaka, A. (2010). Localized projection learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6218 LNCS, pp. 90–99). https://doi.org/10.1007/978-3-642-14980-1_8
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