Scaling the kernel function based on the separating boundary in input space: A data-dependent way for improving the performance of kernel methods

Author:

Sun Jiancheng,Li Xiaohe,Yang Yong,Luo Jianguo,Bai Yaohui

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference35 articles.

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2. F.R. Bach, G.R. Lanckriet, M.I. Jordan, Multiple kernel learning, conic duality, and the SMO algorithm, in: Proceedings of the Twenty-first International Conference on Machine Learning, Banff, Alberta, 2004, pp. 41–48.

3. C. Blake, E. Keogh, C.J. Merz, UCI Repository of Machine Learning Databases. Dept. Inform. Comput. Sci., Univ. California, Irvine, CA. 1998 .

4. On the complexity of learning the kernel matrix;Bousquet;Advances in Neural Information Processing Systems,2003

5. Geometry and invariance in kernel based methods;Burges;Advances in Kernel Methods: Support Vector Learning,1999

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