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
Link To Document :
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