Habituation in Learning Vector Quantization

A modification of Kohonen's Learning Vector Quanti zation is proposed to hand le hard cases of supervised learning with a rugged decision surface or asymmetries in the input dat a structure. Cell reference points (neurons) are forced to move close to the decision surface by successively omit ting input data that do not find a neuron of the opposite class within a circle of shrinking radius . This simulates habituation to frequent but unimportant stimuli and admits problem solving with fewer neurons. Simple estimates for the optimal shrinking schedule and result s of illustrative runs are presented.