Improved kernel minimum squared error method and its implementations

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.