Improved neural network for svm learning - Semantic Scholar

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 5, SEPTEMBER 2002

Improved Neural Network for SVM Learning Davide Anguita and Andrea Boni Abstract—The recurrent network of Xia et al. was proposed for solving quadratic programming problems and was recently adapted to support vector machine (SVM) learning by Tan et al. We show that this formulation contains some unnecessary circuit which, furthermore, can fail to provide the correct value of one of the SVM parameters and suggest how to avoid these drawbacks. Index Terms—Quadratic programming, recurrent networks, support vector machine (SVM).

I. INTRODUCTION The support vector machine (SVM) is one of the most successful learning algorithms proposed in recent years [6]. The basic idea of SVM learning can be easily adapted for classification, regression, and novelty detection tasks, since the SVM shows remarkable properties and generalization ability in all these areas [7], [11], [15]. One of the main advantages of the SVM over other networks is that its training is performed through the solution of a linearly constrained convex quadratic programming problem: therefore, only a global (not necessarily unique) minimum exists and, given a fixed tolerance, efficient algorithms can find an approximate solution in a finite number of steps [8]. Shortly after the proposal of the SVM [6], the interest for hardware implementations has emerged and, at the best knowledge of the authors, the first circuit for SVM learning, suited for analog VLSI, appeared in [1]. The main idea is to define a recurrent network described by the following differential equation:

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learning for classification tasks. Due to space constraints, we do not give all the details, which can be easily found elsewhere [7]. Given a set of patterns fx i ; yi gli=1 with xi 2