摘要
基于RBF核的支持向量机(SVM)模型选择取决于两个参数,即惩罚因子和核参数,为了寻找SVM参数的最优组合,利于笔迹鉴别图像的自动识别,提出了基于混沌序列的参数搜索算法以实现SVM模型参数的自动选择。从与网格法和双线性法进行的比较实验可以看出,基于混沌序列的SVM参数选取更简单,更易于实现,并使SVM具有更好的推广能力。在10人笔迹灰度图像库上分类识别实验结果表明,该方法不但可以提高分类识别率,而且显著减少了训练SVM的个数。
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.
出处
《计算机应用》
CSCD
北大核心
2007年第8期1961-1963,共3页
journal of Computer Applications
关键词
支持向量机
混沌序列
参数选取
笔迹鉴别
Support Vector Machine （SVM）
chaotic series
parameters selection
handwriting verification
