N-tuple or RAM based neural network classification system and method

DETAILED DESCRIPTION Recently Thomas Martini Jørgensen and Christian Linneberg (inventors of this invention) have provided a statistical framework that have made it possible to make a theoretical analysis that relates the expected output scores of the n-tuple net to the stochastic parameters of the example distributions, the number of available training examples, and the number of address lines n used for each LUT or n-tuple.
From the obtained expressions, they have been able to study the behaviour of the architecture in different scenarios.
Furthermore, they have based on the theoretical results come up with proposals for modifying the n-tuple classification scheme in order to make it operate as a close approximation to the maximum a posteriori or maximum likelihood estimator.
The resulting modified decision criteria can for example deal with the so-called skewed class prior problem causing the n-tuple net to often behave poorly when trained on a training set where the distribution of examples between the training classes are highly skewed.
Accordingly the proposed changes of the classification scheme provides an essential improvement of the architecture.
The suggested changes in decision criteria are not only applicable to the original n-tuple architecture based on random memorisation.
It also applies to extended n-tuple schemes, some of which use a more optimal selection of the address lines and some of which apply an extended weight scheme.
According to a first aspect of the present invention there is provided a method for 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 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, said method comprising determining the column vector cell values 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