Method for learning data classification in two separate classes separated by a region divider of order 1 or 2

摘要

A method for learning to classify data according to two distinct classes (c11, c12) separated by a separating surface (S), by means of a neurone of the binary type comprising a parameter describing the separating surface and whose inputs are weighted by a weight (wi), and including the following steps:a) defining a cost function C:b) initializing the weights (Wi), the radii (ri), the parameters (sigma, T+, T-), the learning rate (epsi) and speeds of the temperature decreasing (deltaT+, deltaT-);c) minimizing the cost function C by successive iterations;d) obtaining the values of the weights of the connections and radii of the neurone.Application to the classification and recognition of shapes by a neural network.