A higher-order neural network for distortion invariant pattern recognition

Recently, it is shown that a single layer, higher-order neural network is effective for scale, rotation and shift invariance and in the training process it requires only one example for one category and a very small number of iterations. However, there are problems that scale invariance doesn't hold precisely and it is not so effective for distortion of unknown patterns. In this paper we present an idea to realize the scale invariance precisely and suggest a method that is available to distorted patterns. The experimental results are presented to show the feasibility of our approach.