A self-organizing network for computing a posteriori conditional class probability

A neural network architecture whose goal is the computation of a posteriori conditional class probabilities for input vectors that belong to one of two input classes is described. The network architecture has been designed to adaptively produce Voronoi tessellation partitions of the input vectors in R/sup n/ based on the Euclidean distance metric, without regard to the actual a priori class probabilities of the input vectors. These prior probabilities are then used by the network to adaptively compute the a posteriori conditional class probability for the two classes for each tessellation partition. The network presented is thus a connectionist model for vector quantization clustering and includes the process of automatic node creation necessary for many unsupervised learning applications.<
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