Abstract
In this paper a general kernel optimization model based on kernel Fisher criterion (GKOM) is presented. Via a data-dependent kernel function and maximizing the kernel Fisher criterion, the combination coefficients of different kernels can be learned adaptive to the input data. Finally positive empirical results on benchmark datasets are reported.
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References
Xiong, H.L., Swamy, M.N.S., Ahmad, M.O.: Optimizing the kernel in the empirical feature space. IEEE Trans. Neural Networks 16(2), 460–474 (2005)
Amari, S., Wu, S.: Improving support vector machine classifiers by modifying kernel functions. Neural Networks 12(6), 783–789 (1999)
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© 2006 Springer-Verlag Berlin Heidelberg
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Chen, B., Liu, H., Bao, Z. (2006). General Kernel Optimization Model Based on Kernel Fisher Criterion. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_24
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DOI: https://doi.org/10.1007/11881070_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
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