A Hierarchical ART Network Model for General Pattern Recognition

Ninth Midwest Artificial intelligence and Cognitive Science Conference

Volume

Ninth Midwest Artificial intelligence and Cognitive Science Conference

Contents

A new neural network architecture is introduced which may be used for fault-tolerant general pattern recognition. Images are learned by extracting features at each layer. These same images may later be recognized by extracting features which are then used to constrain a search for additional features to validate one of a set of chosen image representation candidates. Unsupervised learning of feature patterns at each layer is accomplished using adaptive resonance theory (ART1) networks.