Machine learning in vector models of neural networks

Abstract

We present the review of our works related to the theory of vector neural networks. The interconnection matrix always is constructed according to the generalized Hebb's rule, which is well-known in the Machine Learning. We accentuate the main principles and ideas. Analytical calculations are based on the probability approach. The obtained theoretical results are verified with the aid of computer simulations. © 2010 Springer-Verlag Berlin Heidelberg.