Patent ID: 6999950
Filing Date: 2006-02-14
Classification: G06K,G06N

Abstract:
1. A computerized method of training a computer classification system for a network comprising a number of n-tuple or Look Up Tables (LUTs), with each n-tuple or LUT comprising a number of rows corresponding to at least a subset of possible classes and further comprising, a number of columns being addressed by signals or elements of sampled training input data examples, each column being defined by a vector having cells with values, wherein the column vector cell values are determined based on one or more training sets of input data examples for different classes so that at least part of the cells comprise or point to information based on the number of times the corresponding cell address is sampled from one or more sets of training input examples, said method being characterized in that one or more output score functions are determined for evaluation of at least one output score value per class, and one or more decision rules are determined to be used in combination with at least part of the obtained output scores to determine a winning class, wherein said determination of the output score functions and/or decision rules comprises: determining output score functions based on the information of at least part of the determined column vector cell values, and adjusting at least part of the output score functions based on an information measure evaluation, and/or determining decision rules based on the information of at least part of the determined column vector cell values, and adjusting at least part of the decision rules based on an information measure evaluation; and training the computer classification system.