Support Vector Machine Based on Hybrid Kernel Function
Support vector machine use the kernel function to realize from the original input space to a high dimension space nonlinear mapping, and kernel function is the core of support vector machine, it is also the part which is difficult to understand of support vector machine. Because each of ordinary kernel functions has advantages and drawbacks, in order to get another kernel function with strong learning ability and generalization performance, this paper studies two kernel function of support vector machine—global kernel function(linear kernel function) and local kernel function(RBF kernel function), and presents combination kernel function of support vector machine. Through the experiment results comparing, results show that its performance is better than that of other SVMs constructed by ordinary kernel function.
Unable to display preview. Download preview PDF.
- 1.V. N Vapnik: The Nature of Statistical Learning Theory. Springer-Verlag, New York (1995).Google Scholar
- 2.J. Mercer: Functions of positive and negative type and their connection with the theory of integral equations. Trans. Roy. Soc. London, A209: (1990)415–416.Google Scholar
- 3.A. J. Smola: Learning with Kernel, Ph.D. Thesis berlin (1998).Google Scholar
- 4.Smits G F, Jordaan E. M.: Improved SVM Regression using Mixtures of Kernels. Proceedings of the 2002 International Joint Conference on Neural Networks. Hawaii: IEEE, (2002)2785–2790.Google Scholar
- 5.Saunders C, Stitsion Mo, Weston J, Support vector machine reference manual. Technical Report CSD-TR-98-03.Royal Holloway University (1998).Google Scholar
- 6.C. Blake, C. Merz, UCI: repository of machine learning databases, (1998). http://www.ics.uci.edu/~mlearn/MLRepository.html.
- 7.Cristianini N, Shawe-Taylor J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. The Syndicate of the Press of the University of Cambridge (2000).Google Scholar