Abstract
In this paper we present a new model appropriate for pattern recognition tasks. This new model, called αβ Associative Model, arises when taking theoretical elements from the αβ associative memories, and they are merged with several new mathematical transforms. When applied to handwritten digits recognition, namely in the MNIST database, the αβ Associative Model exhibits competitive results against some of the most widely known algorithms currently available in scientific literature.
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López-Leyva, L.O., Yáñez-Márquez, C., Flores-Carapia, R., Camacho-Nieto, O. (2008). Handwritten Digit Classification Based on Alpha-Beta Associative Model. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2008. Lecture Notes in Computer Science, vol 5197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85920-8_54
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DOI: https://doi.org/10.1007/978-3-540-85920-8_54
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