The Hypercube Separation algorithm: A fast and efficient algorithm for on-line handwritten character recognition

This paper introduces a new neural network training algorithm, Hypercube Separation (HCS) algorithm which is very fast and guaranteed to learn. HCS is a simple algorithm suitable for hardware implementation which classifies different input patterns presented to it through the formation of multiple hyperplanes. The performance of the HCS algorithm is compared to that of the Binary Synaptic Weights (BSW) algorithm and to the Backpropagation (BP) algorithm in solving the two spiral problem, which is an almost pathological problem for pattern separation. The HCS algorithm was able to successfully separate the input patterns, requiring three orders of magnitude less training time than the BP algorithm and one order of magnitude less hidden layer nodes than the BSW algorithm. We also present the application of HCS to on-line handwritten character recognition with good results, especially when the simple nature of the algorithm is taken into consideration.