A New Approach for Applying Support Vector Machines in Multiclass Problems Using Class Groupings and Truth Tables

The Support Vector Machines (SVMs) had been showing a high capability of complex hyperplane representation and great generalization power. These characteristics lead to the development of more compact and less computational complex methods than the One-versus-Rest (OvR) and One-versus-One (OvO) [1] classical methods in the application of SVMs in multiclass problems. This paper proposes a new method for this task, named Truth Table Fitting Multiclass SVM (TTF-MCSVM), in which less SVMs are used than other classical methods. The main objective of this research is the development of an efficient method to be applied in problems with very large number of classes, like in the recognition of East Asian languages characters (e.g. Japanese and Chinese kanji).