Parameter Selection Algorithm for Support Vector Machine

Abstract Support Vector Machine (SVM) is a new modeling method. It has shown good performance in many field and mostly outperformed neural networks. The parameter selection should to be done before training SVM. Modified particle swarm optimization (POS) was adpoted to select parameters of SVM. It is shown by simulation that the modified POS algorithm can derive a set of optimal parameters of SVM. Compared with neural networks, SVM model possess some advantages such as simple structure, fast convergence speed with high generalization ability.

[1]  Li Zhang,et al.  Wavelet support vector machine , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[3]  Gary William Flake,et al.  Efficient SVM Regression Training with SMO , 2002, Machine Learning.

[4]  Chih-Jen Lin,et al.  A Study on SMO-Type Decomposition Methods for Support Vector Machines , 2006, IEEE Transactions on Neural Networks.

[5]  Constantin Zopounidis,et al.  Additive Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[7]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[8]  Bernhard Schölkopf,et al.  The connection between regularization operators and support vector kernels , 1998, Neural Networks.