Neural Networks, IEEE - INNS - ENNS International Joint Conference on
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Abstract

Vector quantization consists in finding a discrete approximation of a continuous input. One of the most popular neural algorithms related to vector quantization is the, so-called, Kohonen map. In this paper, we generalize vector quantization to temporal data, introducing context quantization. We propose a recurrent network inspired by the Kohonen map, the Contextual Self-Organizing Map that develops near-optimal representations of context. We demonstrate quantitatively that this algorithm shows better performance than the other neural methods proposed so far.
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