A binary pattern classification using potts model
Boris Kryzhanovsky, Vladimir Kryzhanovsky · Optical Memory and Neural Networks · 2008
The paper offers a new kind of neural network for classifying binary patterns. Given the dimensionality of patterns, the memory capacity of the network grows exponentially with free parameter s . The paper considers the limitations for parameter s caused by the fact that greater values of demand large computer memory and decrease the basin of attraction we have. In contrast to similar models, the network enjoys larger memory capacity and better recognition capabilities—it can distinguish heavily distorted patterns and even cope with pattern correlation. The negative effect of the latter can be easily suppressed by taking a large enough value of s . A perceptron recognition system is considered to demonstrate the efficiency of the algorithm, yet the method is quite applicable in fully connected associative-memory networks.