An Adaptive, Layered Bayes Network
James S. J. Lee, James C. Bezdek · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1989
An adptive pattern recognition network is described that has several internal feature selection layers. Bayes rule combines features and derives each layer from its predecessor starting from two features per node in the first internal layer. Nodes in higher order layers involve more features than those in the lower order layers. Each node in the last internal layer involves all the input features, and is constructed by different feature combinations. A confidence combination layer then combines recognition confidences of the nodes in the last internal layer. This layer dynamically selects only the most significant (weighted) nodes for each class. Our network provides rapid incremental learning from new training samples, dynamic introduction of new classes and new features, and the exclusion of existing classes and features without retraining on the modified data. We illustrate our method by comparing empirical error rates obtained by applying the layered network, a single internal layer network, and the Bayes quadratic decision rule to the ubiquitous IRIS data.