Abstract: The invention relates to n-tuple or RAM based neural network classification methods and systems and, more particularly, to n-tuple or RAM based classification systems where the decision criteria applied to obtain the output sources and compare these output sources to obtain a classification are determined during a training process. Accordingly, the invention relates to a system and a method of training a computer classification system which can be defined by a network comprising a number of n-tuples 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 comprising columns being addressed by signals or elements of sampled training input data examples.

Abstract: A method and system for training a computer classification system which can be defined by a network of a number of n-tuples or Look Up Tables (LUTs), with each n-tuple or LUT including a number of rows corresponding to at least a subset of possible classes and further including 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 sample from one or more sets of training input examples, and weight cell values are determined, corresponding to one or more column vector cells being addressed or sampled by the training examples.