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The study on the optimize algorithm of support vector machine based on variational inequalities. (Chinese. English summary) Zbl 1399.68145

Summary: Since the standard support vector machine model is a quadratic programming, the algorithm process will be more and more complicated with the increasing size of the data. In this article, a new model of a strictly convex quadratic programming is constructed which is based on the K-SVCR algorithm. The main feature of the present model is that it can transform the first-order optimality conditions into the variational inequality problems, and turn complementary problems into smooth equations by the use of the Fischer-Burmeister (FB) function. A more smooth and faster Newton algorithm is established. This article expounds that the generated sequence by the algorithm is global convergent. After testing a standard data set, the efficiency of the algorithm is proposed. The algorithm has better performance on the training accuracy and run-time compared with the K-SVCR algorithm. The results of this experiment indicate that this algorithm is feasible and efficient.

MSC:

68T05 Learning and adaptive systems in artificial intelligence
90C33 Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming)
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