DocumentCode
2703033
Title
Morphological rules of similarity for hierarchical distributed representations
Author
de L. Pereira Castro, J.
Author_Institution
Programa de Comput. Cientifica, Fundacao Oswaldo Cruz., Rio de Janeiro
fYear
2000
fDate
2000
Firstpage
267
Lastpage
272
Abstract
This paper presents and discusses four new criteria that are able to correctly identify hierarchical relationships (in top-down and bottom-up fashion) created upon binary distributed representations. Each criteria is mathematically formulated in terms of two separate rules of similarity, each one being able to identify one type of hierarchical relationship. The rules are also presented in terms of a given frame of reference in accordance with the hierarchical distributed representations. Several mathematical correspondences are shown among different criteria and the rules that compose them. It is proven that the new rules used to identify hierarchical relationships among two patterns represent the mathematical decomposition of the hamming distance among them. The combination of the rules able to identify one type of hierarchical relationship can be used to identify particular cluster of patterns with special meaning. This diminishes the need of defining state spaces with high-dimensionality
Keywords
content-addressable storage; finite state machines; neural nets; pattern recognition; state-space methods; associative memory; binary distributed representations; hamming distance; morphological similarity rules; neural nets; neural state machines; pattern recognition; state spaces; Hamming distance; State-space methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. Proceedings. Sixth Brazilian Symposium on
Conference_Location
Rio de Janeiro, RJ
ISSN
1522-4899
Print_ISBN
0-7695-0856-1
Type
conf
DOI
10.1109/SBRN.2000.889750
Filename
889750
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