Neural networks for determining affinity functions of binary objects

Abstract

A new method of synthesizing neural network for comparing, identifying, and classifying various objects through bipolar encoding of their attributes is offered. The mentioned method broadens the neuron network applicability sphere for solving tasks of identification and classification due to the use of proximity functions applying finer proximity attributes for discrete objects than the Hemming's distance.