24. A method of pattern recognition comprising: receiving a plurality of training input patterns of a data type from a plurality of subject classes during a training operation; forming a set of categories of the training input patterns; generating a category definition for each category according to training input patterns received within the category; counting the number of training input patterns received for each class within each category; for each category, generating a training histogram of the training input patterns received within the category, the training histogram including counts of training input patterns of each class received within the category; receiving at least one test input pattern of the data type from a subject during a testing operation; computing a correlation between a category definition and each test input pattern; forming a category association between each test input pattern and a category based on the correlation; and forming an observation histogram to classify the subject, the observation histogram being formed from each training histogram of each category of each category association and representing counts of training input patterns received by the training subsystem during the training operation, classification of the subject being determined by a peak class of the observation histogram, the peak class representing the highest training input pattern count of the observation histogram.
24. A method of pattern recognition comprising: receiving a plurality of training input patterns of a data type from a plurality of subject classes during a training operation; forming a set of categories of the training input patterns; generating a category definition for each category according to training input patterns received within the category; counting the number of training input patterns received for each class within each category; for each category, generating a training histogram of the training input patterns received within the category, the training histogram including counts of training input patterns of each class received within the category; receiving at least one test input pattern of the data type from a subject during a testing operation; computing a correlation between a category definition and each test input pattern; forming a category association between each test input pattern and a category based on the correlation; and forming an observation histogram to classify the subject, the observation histogram being formed from each training histogram of each category of each category association and representing counts of training input patterns received by the training subsystem during the training operation, classification of the subject being determined by a peak class of the observation histogram, the peak class representing the highest training input pattern count of the observation histogram. 33. The method of claim 24 wherein the training operation includes computing for each category a category class contrast indicative of the degree to which a peak class of the category dominates the category.