Title :
Non-parametric Least Square Support Vector Machine
Author :
Guo, Ning ; Chen, Xiankai ; Ma, Yingdong ; Chen, Gorge
Author_Institution :
Center of Digital Media Comput., Shenzhen Inst. of Adv. Technol., Shenzhen, China
Abstract :
Distinguished from Semi-Definite Programming (SDP) and Quadratically Constrained Quadratic Program (QCQP) which are classical solutions of multiple kernel learning, we apply Semi-Infinite Linear Program (SILP) to deal with multiple kernels learning in Least Square Support Vector Machine (LSSVM). Furthermore the regularization parameter is added as an extra variable to learn. This algorithm avoids the computational cost consuming by cross validation and make algorithm more convenient and practical.
Keywords :
learning (artificial intelligence); least squares approximations; linear programming; quadratic programming; support vector machines; LSSVM; QCQP; SDP; SILP; cross validation; multiple kernel learning; nonparametric least square support vector machine; quadratically constrained quadratic program; regularization parameter; semidefinite programming; semiinfinite linear program; Accuracy; Classification algorithms; Kernel; Machine learning; Scalability; Support vector machines; Training; LSSVM; MKL; QCQP; SDP; SILP optimal solution;
Conference_Titel :
Natural Computation (ICNC), 2012 Eighth International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4577-2130-4
DOI :
10.1109/ICNC.2012.6234737
