Abstract
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.
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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: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2012. Lecture Notes in Computer Science, vol 7441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33275-3_60
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DOI: https://doi.org/10.1007/978-3-642-33275-3_60
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