Estimation of Probability Density Using Signature Tables for Application to Pattern Recognition

摘要

Signature table training method consists of cumulative evaluation of a function (such as a probability density) at pre-assigned co-ordinate values of input parameters to the table. The training is conditional; based on a binary valued learning input to a table which is compared to the label attached to each training sample. Interpretation of an unknown sample vector is then equivalent of a table look-up, i.e. extraction of the function value stored at the proper co-ordinates. Such a technique is very useful when a large number of samples must be interpreted as in the case of speech recognition and the time required for the training as well as for the recognition is at a premium. (Modified author abstract)