A New Learning Algorithm of SVM from Linear Separable Samples

Training a SVM corresponds to solving a linearly constrained quadratic problem (QP) in a number of variables equal to the number of data points, this optimization problem becoming challenging when the number of data points exceeds few thousands. Because the computational complexity of the existing algorithms is extremely large in case of few thousands support vectors and therefore the SVM QP-problem becomes intractable, several decomposition algorithms that do not make assumptions on the expected number of support vectors have been proposed instead. In this paper we propose a heuristic learning algorithm of gradient type for learning a SVM using linear separable data, and analyze its performance in terms of accuracy and efficiency. In order to evaluate the efficiency of our learning method, several tests were performed against the Platt’s SMO method, and the conclusions are formulated in the final section of the paper.