Transforming fundamental set of patterns to a canonical form to improve pattern recall

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Abstract

Most results (lemmas and theorems) providing conditions under which associative memories are able to perfectly recall patterns of a fundamental set are very restrictive in most practical applications. In this note we describe a simple but effective procedure to transform a fundamental set of patterns (FSP) to a canonical form that fulfils the propositions. This way pattern recall is strongly improved. We provide numerical and real examples to reinforce the proposal. © Springer-Verlag Berlin Heidelberg 2004.

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Sossa, H., Barrón, R., & Vázquez, R. A. (2004). Transforming fundamental set of patterns to a canonical form to improve pattern recall. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3315, pp. 687–696). Springer Verlag. https://doi.org/10.1007/978-3-540-30498-2_69

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