Chaotic particle swarm optimization algorithm for support vector machine

摘要

Statistical Learning Theory focuses on the machine learning theory for small samples. Support vector machine (SVM) are new methods based on statistical learning theory. There are many kinds of function can be used for kernel of SVM. Wavelet function is a set of bases that can approximate arbitrary functions in arbitrary precision. So Marr wavelet was used to construct wavelet kernel. On the other hand, the parameter selection should to be done before training WSVM. Modified chaotic particle swarm optimization (CPOS) was adopted to select parameters of SVM. It is shown by simulation that the CPOS algorithm can derive a set of optimal parameters of WSVM, and WSVM model possess some advantages such as simple structure, fast convergence speed with high generalization ability.