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\iteman{io-port 05787360}
\itemau{Tsuji, Kazuki; Kudo, Mineichi; Tanaka, Akira}
\itemti{Localized projection learning.}
\itemso{Hancock, Edwin R. (ed.) et al., Structural, syntactic, and statistical pattern recognition. Joint IAPR international workshop, SSPR {\&} SPR 2010, Cesme, Izmir, Turkey, August 18--20, 2010. Proceedings. Berlin: Springer (ISBN 978-3-642-14979-5/pbk). Lecture Notes in Computer Science 6218, 90-99 (2010).}
\itemab
Summary: 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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\itemli{doi:10.1007/978-3-642-14980-1\_8}
\end