Abstract
In this work we have reformulated the twin support vector machine (TWSVM) classifier by considering unity norm of the normal vector of the hyperplanes as the constraints. TWSVM with unity norm hyperplanes removes the shortcomings of the classical TWSVM formulation. The resulting new formulation is a nonlinear programming problem which is solved by sequential quadratic optimization method. The performance of the modified classifier verified experimentally on synthetic as well as on benchmark data sets.
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