An empirical comparison of ID3 and HONNs for distortion invariant object recognition

Abstract

The authors present results of experiments comparing the performance of the ID3 symbolic learning algorithm with a higher-order neural network (HONN) in the distortion invariant object recognition domain. In this domain, the classification algorithm needs to be able to distinguish between two objects regardless of their position in the input field, their in-plane rotation, or their scale. It is shown that HONNs are superior to ID3 with respect to recognition accuracy, whereas, on a sequential machine, ID3 classifies examples faster once trained. A further advantage of HONNs is the small training set required. HONNs can be trained on just one view of each object, whereas ID3 needs an exhaustive training set.<>