原空间中最小二乘支持向量机的新算法

Abstract

Abstract: In order to solve the shortcoming of lacking sparseness in the solution of least squares support vector machine in the primal,the pruning method,multi-fold cross validation(MFCV) method and improved multi-fold cross validation(IMFCV) method are proposed and the backward deletion feature selection(BDFS) method is modified and applied.These methods all realize the sparseness of the least squares support vector machine to a certain degree in the primal for a system without outliers.The predicted time of BDFS is shorter than the pruning’s;although the training time of BDFS is shorter than MFCV’s,its predicted time is longer.In the face of a system with outliers,the pruning almost loses effectiveness,and the training time of BDFS and MFCV are both shorter than IMFCV’s,but IMFCV can oppress outliers and reduces the predicted time remarkably.

CLC Number:

TP301

ZHAO Yong-ping, SUN Jian-guo. New method for least squares support vector machine in the primal[J]. Journal of Systems Engineering and Electronics, 2009, 31(01): 142-145,237.

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New method for least squares support vector machine in the primal

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