Models & Optimisation and Mathematical Analysis Journal
Volume 2, Numéro 2, Pages 1-6
2012-12-25
Classification With Support Vector Machines, New Quadratic Programming Algorithm
Authors : Chikhaoui Ahmed . Mokhtari Abdelkader .
Abstract
Support vector machines (SVM) are excellent tools for classification and regression. They seek the optimal separating hyperplan and maximal margin. The modeling results often lead to solving a quadratic programming problem. In this paper, we present a simple method to determine the hyperplan H that separates two classes of examples so that the distance between these two classes is maximal. This method is based on the geometric interpretation of the norm of a linear mapping. The result model of our algorithm modeling is a maximization of a concave quadratic program. This quadratic program is resolved by projection method. Example illustrates the method.
Keywords
Support vector machines, separating hyperplan, maximizing concave function, cosine, projection method.
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