Title :
Learning with Multi-kernel Growing Support Vector Classifiers
Author :
Zhou Jian-guo ; Wang Xiao-wei
Author_Institution :
Dept. of Bus. Adm., North China Electr. Power Univ., Baoding
Abstract :
Support vector machine (SVM) provides accurate classification but suffers from a large amount of computation. In this paper we propose here an incremental procedure for growing support vector classifiers, which serves to avoid a priori architecture estimation or the application of a pruning mechanism after SVM training. The proposed growing approach also opens up new possibilities for dealing with multi-kernel machines, and automatic selection of hyperparameters. At last, the performance of the proposed algorithm and its extensions is evaluated by an experiment
Keywords :
learning (artificial intelligence); pattern classification; support vector machines; architecture estimation; multikernel growing support vector classifiers; multikernel machines; pruning mechanism; Computer architecture; Costs; Kernel; Machine learning; Machine learning algorithms; Pattern recognition; Quadratic programming; Static VAr compensators; Support vector machine classification; Support vector machines;
Conference_Titel :
Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on
Conference_Location :
Jinan
Print_ISBN :
0-7695-2528-8
DOI :
10.1109/ISDA.2006.183