On-line twin independent support vector machines.

The success of SVM in solving pattern recognition problems has encouraged researcher to extend the development of different versions. They are well-known for their robustness and good generalization performance. In many real-world applications, the data to be trained are available on-line in a sequential fashion and because of space and time requirements, batch training methods are not suitable. This paper proposes a new fast on-line algorithm called OTWISVM. It defines two optimization problems and incremental learning is done based of them. Two hyperplanes are generated as decision functions thus each of them is closer to one of the two classes and is as far as possible from the other. The solution is constructed via two subsets of linearly independent samples seen so far, and is always bounded. Good accuracy and notable speed of the method was tested and affirmed both on ordinary and noisy data sets as opposed to similar algorithms. [ABSTRACT FROM AUTHOR]