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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Keywords
- Support Vector Machine
- Training Sample
- Recognition Rate
- Synthetic Dataset
- Reproduce Kernel Hilbert Space
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Tsuji, K., Kudo, M., Tanaka, A. (2010). Localized Projection Learning. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2010. Lecture Notes in Computer Science, vol 6218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14980-1_8
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DOI: https://doi.org/10.1007/978-3-642-14980-1_8
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