Generalized connectionist associative memory

Authors:
Nigel Duffy;Arun Jagota
Affiliations:
Department of Computer Science, University of California, Santa Cruz, CA;Department of Computer Science, University of California, Santa Cruz, CA
Venue:
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Year:
1999

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Abstract

This paper presents a generalized associative memory model, which stores a collection of tuples whose components are sets rather than scalars. It is shown that all library patterns are stored stably. On the other hand spurious memories may develop. Applications of this model to storage and retrieval of naturally-arising generalized sequences in bioinformatics are presented. The model is shown to work well for detection of novel generalized sequences against a large database of stored sequences, and for removal of noisy black pixels in a probe image against a very large set of stored images.