X-tron: an incremental connectionist model for category perception

Abstract

A connectionist model for categorization (self-organization) even in the presence of multiple or mixed patterns has been presented. During self-organization, the network automatically adjusts the number of nodes in the hidden and output layers, depending on the complexity or nature of overlap between the patterns. An ambiguity measure is given based on how well the features are being interpreted by the network. From the ambiguity measure a certainty factor about the decision of the network is derived. The effect of noise on the certainty factor is investigated. A vigilance threshold is used to decide whether the network's decision is correct or not. Functionally the network consists of two parts, one of them categorizes the incoming patterns and the other monitors the performance of categorization. The characteristics of the model has also been demonstrated experimentally on both 1D binary strings and image patterns even when they are corrupted by additive, subtractive, and mixed noise.