改进的KMSE方法及其实现

Improved Kernel Minimum Squared Error Method and Its Implementations

依据 KMSE 模型对应的特征空间中的鉴别矢量可表示为部分训练样本的线性组合这一理论前提,可利用回归分析中变量选择的思路对 KMSE 模型加以改进.在本文中为了提高 KMSE 的分类效率而发展出的基于最小平方误差准则的算法能大大提升 KMSE 模型的分类速度.实验结果显示该算法还能取得较优的分类性能.

On the basis of the fact that the discriminant vector of the feature space associated with the kernel minimum squared error （KMSE） model can be expressed in terms of a linear combination of samples selected from all the training samples, the idea of variable selection can be exploited to improve the KMSE model. To improve the classification efficiency, an algorithm based on the minimum square error criterion is proposed. It classifies test samples efficiently. Experiments show that the proposed method also has good classification performance.