An Adaptive Multicategory Pattern Classification System

摘要

An adaptive, multicategory, pattern classification system for classifying statistical patterns is formulated. The system finds application in those instances when the probability densities and a priori probabilities of occurrence of the classes are unknown. The convergence rate and other special properties of the system are examined, including the special case where the expected loss due to misclassification by the system tends to the minimum expected loss which results when using the Bayes discriminant functions. In addition, a simulation of the system for a three-category problem using quadratic discriminant functions is presented. (Author)