A New Approach for Applying Support Vector Machines in Multiclass Problems Using Class Groupings and Truth Tables
نویسندگان
چکیده
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 a new 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). The TTF-MCSVM is based on the combination of many simple binary SVMs, like the OvR and OvO. The N classes are divided in M combinations of two groups, where M is: M = dlog2 Ne (1)
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