Abstract
In Support Vector Machine (SVM), Kernels are employed to map the nonlinear model into a higher dimensional feature space where the linear learning is adopted. The characteristics of kernels have great impacts on learning and predictive results of SVM. Considering the characteristics for fitting and generalization of two kinds of typical kernels–global kernel (polynomial kernel) and local kernel (RBF kernel), a new kind of SVM modeling method based on composite kernels is proposed. In order to evaluate the reasonable fitness of kernel functions, the particle swarm optimization (PSO) algorithm is used to adaptively evolve SVM to obtain the best prediction performance, in which each particle represented as a real vector corresponds to a set of the candidate parameters of SVM. Experiments in time series prediction demonstrate that the SVM with composite kernels has the better performance than with a single kernel.
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References
Vapnik, V.N.: On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities. Soviet Mathematics: Doklady 9, 915–918 (1968)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Mukherjee, S., Osuna, E., Girosi, F.: Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines. In: Proc. of IEEE NNSP’97, Amelia Island, FL (1997)
Mukherjee, S., Osuna, E., Girosi, F.: Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines. In: NNSP’ 97: Neural Networks for Signal Processing VII: Proceedings of the IEEE Signal Processing Society Workshop, Amelia Island, FL, USA, pp. 511–520 (1999)
Tay, F.E.H., Cao, L.J.: Application of Support Vector Machines in Financial Time Series Forecasting. Omega 29(4), 309–317 (2001)
Scholkopf, B., Mika, S., Burges, C.J.C., Knirsch, P., Muller, K.R., Ratsch, G., Smola, A.J.: Input Space Versus Feature Space in Kernel-Based Methods. IEEE Trans. Neural Networks 10, 1000–1017 (1999)
Mercer, J.: Functions of Positive and Negative Type and their Connection with the Theory of Integral Equations. Philos. Trans. Roy. Soc. London A 209, 415–446 (1909)
Smola, A.: Learning with Kernels. Ph.D. thesis, GMD, Birlinghoven (1999)
Smits, G.F., Jordaan, E.M.: Improved SVM Regression Using Mixtures of Kernels. In: Proc. of IJCNN 02 on Neural Networks, vol. 3, pp. 2785–2790 (2002)
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Jiang, T., Wang, S., Wei, R. (2007). Support Vector Machine with Composite Kernels for Time Series Prediction. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_45
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DOI: https://doi.org/10.1007/978-3-540-72395-0_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
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