An unsupervised hyperspheric multilayer feedforward neural network classifier
Daniel N. Nissani · 2002
A neural network model intended for the classification of patterns into distinct categories is introduced. Arbitrarily accurate category formation in a predefined feature space is asymptotically achieved by means of an unsupervised learning algorithm. Two model variants, one under a category separability assumption and the other under a category probability density unimodality (and nonseparability) assumption, are suggested. The hyperspheric nature (as opposed to hyperplanar, typical of some current classifiers) of this model and its multilayer feedforward architecture are explained. Simulation results demonstrating asymptotic convergence and excellent classification accuracy are provided.>