On the design of a hardware-software architecture for acceleration of SVM's training phase
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Clock power minimization using structured latch templates and decision tree induction
Proceedings of the International Conference on Computer-Aided Design
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Learning from data is one of the basic ways humans perceive the world and acquire the knowledge. Support vector machine (SVM for short) has emerged as a good classification technique and achieved excellent generalization performance in a variety of applications. Training SVM on a dataset of huge size with millions of data is a challenging problem since it is computationally expensive and the memory requirement grows with the square of the number of training examples. This paper surveys SVM training algorithms and falls them into three groups. Moreover, recent advances such as finite Newton method and active learning algorithms are described.