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A Generalized Unsupervised Competitive Learning Scheme
Ferdinand PEPER Hideki NODA
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1993/05/25
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks
neural networks, modeling and simulation,
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In this article a Neural Network learning scheme is described, which is a generalization of VQ (Vector Quantization) and ART2a (a simplified version of Adaptive Resonance Theory 2). The basic differences between VQ and ART2a will be exhibited and it will be shown how these differences are covered by the generalized scheme. The generalized scheme enables a rich set of variations on VQ and ART2a. One such variation uses the expression ||I||2+||zj||2/||zj||sin(I,zj), as the distance measure between input vector I and weight vector zj. This variation tends to be more robust to noise than ART2a, as is shown by experiments we performed. These experiments use the same data-set as the ART2a experiments in Ref.(3).