Self-learning neural M-ary tree classifier
Z. Wang, J. Hanson · 1991
A novel version of a multilayer neural network, called the self-learning neural model (SLNM), is presented. The different level structures, dynamics, and learning strategies of the SLNM are investigated. This neural model can be used as adaptive nonparametric neural-net classifiers or clusters, which can be trained by unlabeled data. An M-ary decision tree structured classifier with the building blocks of this type of neural networks is developed. The M-ary tree classifiers are systems of loosely coupled hybrid neural networks and adaptive nonparametric neural-net classifiers. Two types of the M-ary tree classifiers are discussed. Their preliminary simulations have shown very encouraging results.>