支持向量分类和多宽度高斯核

支持向量分类中，高斯核不区分样本中各个特征的重要性，显然各个特征对分类的贡献一般是不相同的。为了体现这种差别从而提高支持向量机的泛化性能，文中提出了多宽度高斯核的概念。多宽度高斯核增加了支持向量机的超级参数，进一步地，文中提出了多参数模型选择算法。算法利用误差界自动实现模型选择。通过实验验证了多宽度高斯核和多参数模型选择算法的有效性。

In support vector classification, Gaussian kernel is insensitive to the differences of features. However, generally, different features function differently in classification. To improve the generalization performance of support vector machines, the Gaussian kernel with multiple widths is proposed to emphasize the different contributions of features to classification. With this kernel, the related model selection scheme is designed which can automatically tune multiple parameters for support vector machines by minimizing the error bound. The efficiencies of the proposed kernel and related model selection algorithms are validated via experiments.