Improved sparse LSSVMS based on the localized generalization error model

Abstract

The least squares support vector machine (LSSVM) is computationally efficient because it converts the quadratic programming problem in the training of SVM to a linear programming problem. The sparse LSSVM is proposed to promote the predictive speed and generalization capability. In this paper, two sparse LSSVM algorithms: the SMRLSSVM and the RQRLSSVM are proposed based on the Localized Generalization Error of the LSSVM. Experimental results show that the RQRLSSVM yields both better generalization capability and sparseness in comparison to other sparse LSSVM algorithms.

Lab Members

Patrick Chan

Associate Professor, Vice Dean

Patrick Chan works on machine learning, deep learning, image processing, adversarial learning, and secure machine learning.