Parallel Implementation of Gradient-Based Neural Networks for SVM Training

This paper presents the implementation of two neural networks for SVM training in parallel computers. The results obtained are compared with two well known packages for SVM training and the parallel implementation shows that the neural network approach can be as accurate as the traditional packages and, since the proposed gradient-based neural networks can be easily parallelized, the proposed approach is scalable and the training times can be considerably reduced.