一种SVM分类器自动模型选择方法

Automatic model selection method for support vector machines classifiers

提出了一种基于粗网格与模式搜索相结合的支持向量机分类器模型参数优化方法,采用Jaakkola-Haussler误差上界作为模型选择的评价标准。以黎曼几何为理论依据,提出了一种新的保角变换,对核函数进行数据依赖性改进,进一步提高分类器泛化能力。在研究人工非线性分类问题的基础上,将该方法应用于手写相似汉字识别,实验结果表明分类精度得到了明显提高。

An optimal approach was presented for model parameters of a support vector machine classifier based on coarse grid search combined with pattern search, in which the Jaakkola-Haussler error bound was considered as the evaluation criterion of model selection. Based on the Riemannian geometry theory, a novel conformal transformation was proposed and the kernel function was modified by the transformation in a data-dependent way. Simulated results for the artificial data set showed that the approach for automatic model selection was very effective. An application of the approach in handwritten similar Chinese characters recognition was further investigated. The experimental result showed remarkable improvement of the performance of the classifier.