TRUST-TECH Based Neural Network Training

Efficient training in a neural network plays a vital role in deciding the network architecture and the accuracy of these classifiers. Most popular local training algorithms tend to be greedy and hence get stuck at the nearest local minimum of the error surface and this corresponds to suboptimal network model. Stochastic approaches in combination with local methods are used to obtain an effective set of training parameters. Due to the lack of effective fine-tuning capability, these algorithms often fail to obtain such an optimal set of parameters and are computationally expensive. As a trade-off between computational expense and accuracy required, a novel method to improve the local search capability of training algorithms is proposed in this paper. This approach takes advantage of TRUST-TECH (TRansformation Under STability-reTaining Equilibrium CHaracterization) to compute neighborhood local minima on the error surface surrounding the current solution in a systematic manner. Empirical results on different real world datasets indicate that the proposed algorithm is computationally effective in obtaining promising solutions.