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, or credit 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.
Marshall
J A
JA
Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599-3175, USA. Marshall@cs.unc.edu
Gupta
V S
VS
D003198:Computer Simulation; D005795:Generalization, Stimulus / Q000502:physiology*; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer