A Swarm Intelligence Approach to SVM Training
نویسندگان
چکیده
In this paper we outline a new swarm intelligence based approach to the problem of training support vector machines with non positive definite kernels. Past approaches using particle swarm optimizers have been shown to compare poorly with other evolutionary computation based methods. In this paper, we describe a new heterogeneous particle swarm optimizer, specifically tailored for the training of support vector machines. We present experimental results of the comparison of this algorithm with traditional support vector machine training algorithms and recent evolutionary approaches. The comparison is made both with positive definite and non positive definite kernels. The new algorithm is shown to be competitive with all approaches in terms of final classification accuracy and the fastest of the evolutionary computation based algorithms. The results also suggest that the evolutionary and swarm intelligence optimizers can achieve better classification results that the traditional methods when non positive definite kernels are used.
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