Patent ID: 6510245
Filing Date: 2003-01-21
Classification: G06K

Abstract:
A classification model generating method characterized by comprising the steps of:when n-dimensional data which belongs to one class in an n-dimensional feature space defined by n types of variates and whose position is specified by the variates is input, dividing the feature space into mn divided areas by performing m-part division for each of the variates, and determining a division number m on the basis of a statistical significance level in the division by regarding a degree of generation of a divided area containing one data as a degree following a probability distribution with respect to the division number m; setting a divided area containing n-dimensional data as a learning area belonging to the class, and associating each input data with a corresponding divided area; adding divided areas around the learning area as learning areas to expand a learning area group; and removing a learning area located on a boundary between the learning area and a divided area which is not a learning area from the learning area group to contract the learning area group.