Analysis of input vector space for speeding up learning in feedforward neural networks

Although several new learning rules have been proposed, the space and time complexities of these two bottlenecks remain. The bottlenecks occur because all the existing learning techniques are based on a single large neural structure. In this paper, the authors approach the solution to this problem by analyzing the input vector space and, then, partition this space into subspaces. Each subspace will be learned by a small neural network with a simple learning rule. The authors also speed up the learning process by reducing the number of training vectors down to O(mn) instead of 2ⁿ, where m is the number of vectors having output targets equal to ones and n is the number of input bits. The reduction is based on the concept of guard ring vectors. The experimental results show that the learning can be speeded up by 2-30 times over the non-partition process. However the number of neurons used in the authors´ approach is uncontrollable. In some cases, the number is reduced but in some cases it is increased