A Survey on Training Algorithms for Support Vector Machine Classifiers

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.