Title :
The Optimization of Large Scale Multiple Kernel SVM Based on K-Means Clustering in Kernel Space
Author :
Qin Hua ; Zhang Min ; Qin Xi ; Su Yi-dan
Author_Institution :
Comput. Sci. Dept., Guangxi Univ., Nanning, China
Abstract :
The generalization ability of multiple kernel support vector machines is better than the single kernel ones. If the training datasets are large scale, solving the optimal multiple kernels´ combination coefficients with semidefinite programming method is difficult, and the time-consuming is large. We use K-means Clustering algorithm in kernel space to reduce the scale of SVM´s training datasets, then the scale of the corresponding semidefinite programming is reduced. Our experimental results show that: the new method received more than several times faster than the old one in solving the semidefinite programming problem of SVM, and does not reduce the classification accuracy of multi-kernel SVM model.
Keywords :
learning (artificial intelligence); linear programming; pattern clustering; statistical analysis; support vector machines; K-means clustering; kernel space; large scale multiple kernel SVM; optimization; semidefinite programming method; training dataset; Computers; Educational institutions; Kernel; Machine learning; Programming; Support vector machines;
Conference_Titel :
Internet Technology and Applications, 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5142-5
Electronic_ISBN :
978-1-4244-5143-2
DOI :
10.1109/ITAPP.2010.5566082