An associative memory is a binary relationship between inputs and outputs, which is stored in an M matrix. In this paper, we propose a modification of the Steinbuch Lernmatrix model in order to process real-valued patterns, avoiding binarization processes and reducing computational burden. The proposed model is used in experiments with noisy environments, where the performance and efficiency of the memory is proven. A comparison between the proposed and the original model shows a good response and efficiency in the classification process of the new Lernmatrix. © 2012 Springer-Verlag.
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Carbajal-Hernández, J. J., Sánchez-Fernández, L. P., Sánchez-Pérez, L. A., Carrasco-Ochoa, J. A., & Martínez-Trinidad, J. F. (2012). A modification of the lernmatrix for real valued data processing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7441 LNCS, pp. 487–494). https://doi.org/10.1007/978-3-642-33275-3_60
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