Abstract
Application of Linear Programming for binary perceptron learning allows reaching theoretical maximum loading of the perceptron that had been predicted by E. Gardner. In the present paper the idea of learning using Linear Programming is extended to vector multistate neural networks. Computer modeling shows that the probability of false identification for the proposed learning rule decreases by up to 50 times compared to the Hebb one.
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Kryzhanovskiy, V., Zhelavskaya, I., Fonarev, A. (2012). Vector Perceptron Learning Algorithm Using Linear Programming. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_25
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DOI: https://doi.org/10.1007/978-3-642-33266-1_25
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