1. A pattern recognition system comprising: a training subsystem that receives during a training operation a plurality of training input patterns of a data type from a plurality of subject classes, that forms a set of categories of the training input patterns, that assigns each category a category definition according to training input patterns received within the category, that counts the training input patterns received for each class within each category and that generates for each category a training histogram of the training input patterns received within the category, the training histogram including counts of training input patterns received for each class within the category; and a classifier that receives during a testing operation at least one test input pattern of the data type from the subject, that accesses the set of categories and computes a correlation between a category definition and each test input pattern, that forms a category association between each test input pattern and a category based on the correlation and that forms 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.
1. A pattern recognition system comprising: a training subsystem that receives during a training operation a plurality of training input patterns of a data type from a plurality of subject classes, that forms a set of categories of the training input patterns, that assigns each category a category definition according to training input patterns received within the category, that counts the training input patterns received for each class within each category and that generates for each category a training histogram of the training input patterns received within the category, the training histogram including counts of training input patterns received for each class within the category; and a classifier that receives during a testing operation at least one test input pattern of the data type from the subject, that accesses the set of categories and computes a correlation between a category definition and each test input pattern, that forms a category association between each test input pattern and a category based on the correlation and that forms 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. 13. The pattern recognition system of claim 1 further comprising: a second training subsystem that receives a second plurality of training input patterns of a second data type from the subject classes and forms a second set of categories; a second classifier that receives at least one test input pattern of the second data type from the subject during the testing operation and forms a category association between the at least one test input pattern of the second data type and a category of the second set of categories and forms a second observation histogram; and a processor for combining the first and second observation histograms into a cumulative histogram to produce a cumulative classification of the subject within a class.