Abstract
In this paper we propose some methods to build a kernel matrix for classification purposes using Support Vector Machines (SVMs) by fusing polynomial kernels. The proposed techniques have been successfully evaluated on artificial and real data sets. The new methods outperform the best individual kernel under consideration and they can be used as an alternative to the parameter selection problem in polynomial kernel methods.
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References
Ali, S., Smith, K.A.: Automatic parameter selection for polynomial kernel. In: Proceedings of the 2003 IEEE International Conference on Information Reuse and Integration, IRI-2003, pp. 243–249 (2003)
Amari, S., Wu, S.: Improving support vector machine classifiers by modifying kernel functions. Neural Networks 12, 783–789 (1999)
Blake, C.L., Merz, C.J.: Uci repository of machine learning databases. university of carolina, Irvine, Department of Information and Computer Sciences (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Bousquet, O., Herrmann, D.J.L.: On the complexity of learning the kernel matrix. In: Becker, S., Thurn, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems, vol. 15, pp. 415–422. The MIT Press, Cambridge (2003)
Gower, J.C., Legendre, P.: Metric and euclidean properties of dissimilarity coefficients. Journal of Classification 3, 5–48 (1986)
Lanckriet, G.R.G., Cristianini, N., Barlett, P., El Ghaoui, L., Jordan, M.I.: Learning the kernel matrix with semi-definite programming. Journal of Machine Learning Research 5, 27–72 (2004)
Lehmann, E.L.: Nonparametrics: Statistical Methods Based on Ranks. McGraw-Hill, New York (1975)
Moguerza, J.M., Martín de Diego, I., Muñoz, A.: Improving support vector classificacion via the combination of multiple sources of information. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR&SPR 2004. LNCS, vol. 3138, pp. 592–600. Springer, Heidelberg (2004)
Ou, Y.-Y., Chen, C.-Y., Hwang, S.-C., Oyang, Y.-J.: Expediting model selection for support vector machines based on data reduction. In: IEEE International Conference on Systems, Man and Cybernetics, p. 786 (2003)
Pȩkalska, E., Duin, R.P.W.: The Dissimilarity Representation for Pattern Recognition. Foundations and Applications. World Scientific, Singapore (2005)
Pękalska, E., Duin, R.P.W., Günter, S., Bunke, H.: On not making dissimilarities euclidean. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR&SPR 2004. LNCS, vol. 3138, pp. 1145–1154. Springer, Heidelberg (2004)
Pȩkalska, E., Paclík, P., Duin, R.P.W.: A generalized kernel approach to dissimilarity-based classification. Journal of Machine Learning Research, Special Issue on Kernel Methods 2(12), 175–211 (2001)
Silverman, B.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)
Vandenberghe, L., Boyd, S.: Semidefinite programming. SIAM Review 38(1), 49–95 (1996)
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de Diego, I.M., Moguerza, J.M., Muñoz, A. (2006). On the Fusion of Polynomial Kernels for Support Vector Classifiers. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_40
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DOI: https://doi.org/10.1007/11875581_40
Publisher Name: Springer, Berlin, Heidelberg
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