基于准则的KMSE分类模型的改造

Kernel Minimum Squared Error model's improvement based on criterion

在再生核理论基础之上,可认为KMSE模型对应的特征空间的鉴别向量可以表示为部分训练样本的线性组合。可据此对一般的KMSE方法(GKMSE)通过某些手段加以改进。文章的准则被首次提出并应用于KMSE的改造,据此提出的改进的KMSE方法在很大程度上提高了KMSE模型的分类效率,同时实验结果也证明了该算法具有比较好的分类效果。

According to the reproducing kernel theory,the discriminant vector in the feature space associated with Kernel Minimum Squared Error （KMSE） model can be approximately expressed in terms of a linear combination of samples selected from all of the training samples.This implies that the general KMSE can be improved for more efficient implementation.The criterion is proposed for the first time in the paper,and then an improved kernel minimum squared error algorithm has been developed based on the criterion,and the experiments show that the proposed method not only is simple,efficient,but also has good performance.