Multiple Kernel Learning for Quadratically Constrained MAP Classification
Author:
Affiliation:
1. The Univeristy of Electro-Communications
2. Brain Science Institute, RIKEN
3. Tokyo Institute of Technology
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Link
https://www.jstage.jst.go.jp/article/transinf/E97.D/5/E97.D_1340/_pdf
Reference7 articles.
1. [1] M. Gönen and E. Alpaydin, “Multiple kernel learning algorithms,” J. Machine Learning Research, vol.12, pp.2211-2268, 2011.
2. [2] T. Yokota and Y. Yamashita, “A quadratically constrained MAP classifier using the mixture of Gaussian models as a weighted function,” IEEE Trans. Neural Networks and Learning Systems, vol.24, no.7, pp.1127-1140, 2013.
3. [3] S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge Univeristy Press, 2004.
4. [4] A. Demiriz, K. Bennett, and J. Shawe-Taylor, “Linear programming boosting via column generation,” J. Machine Learning Research, vol.46, no.1-3, pp.225-254, 2002.
5. [5] S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf, “Learge scale multiple kernel learning,” J. Machine Learning Research, vol.7, pp.1531-1565, 2006.
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