Improved LS-SVM based classifier design and its application

For least squares support vector machine (LS-SVM) classifier to the loss of sparseness and generalization, a pruning modeling method is proposed based on Quadratic Renyi entropy. The kernel principal component is adopted for data pre-processing, and the training set is divided randomly. Then the concept of quadratic Renyi entropy is introduced as the basis of training and pruning in LS-SVM classifier. UCI typical datasets of classification are used for testing the performance of this new model. Experimental results show that the new algorithm takes full account the location of the Lagrange multiplier, thus the sparseness and generalization ability of the classifier can be improved.