Selection of SVM parameters using chaotic series and its application in handwriting verification
基于混沌序列的SVM参数选择及其在笔迹鉴别中的应用

In order to find the optimization compound of Support Vector Machine (SVM) parameters, that is penalty factor and nuclear factor, and help to identify the handwriting image, a parameter searching algorithm based on chaotic sequence was proposed to determine the SVM parameters automatically. Compared with the grid search and two-line search, the proposed algorithm is much simpler and easier to be implemented, which makes SVM has better outreach capacity. Classification experiment on 10 people handwriting gray-scale images prove that the proposed algorithm has higher classification rate and significantly reduce the number of training SVM.