Generalization and Exclusive Allocation of Credit in Unsupervised Category Learning

. A new way of measuring generalization in unsupervised learning is presented. The measure is based on an exclusive allocation,orcredit assignment, criterion. In a classifier that satisfies the criterion, input patterns are parsed so that the credit for each input feature is assigned exclusively to one of multiple, possibly overlapping, output categories. Such a classifier achieves context-sensitive, global representations of pattern data. Two additional constraints, sequence masking and uncertainty multiplexing, are described; these can be used to refine the measure of generalization. The generalization performance of EXIN networks, winner-take-all competitive learning networks, linear decorrelator networks, and Nigrin's SONNET-2 network are compared. 1. Generalization in unsupervised learning The concept of generalization in pattern classification has been extensively treated in the literature on supervised learning, but rather little has been written on generalization with regard t..