NDLI: A dynamic mapping based on probabilistic relaxation

Content Provider	IEEE Xplore Digital Library
Author	Fu, A.M.N. Mong Yan
Copyright Year	1999
Description	Author affiliation: Sch. of Electr. & Inf. Eng., Sydney Univ., NSW, Australia (Fu, A.M.N.)
Abstract	A dynamic mapping is presented, which is defined by a set of iterative equations. It is shown that the mapping maps the domain space which is composed of all m-dimensional probabilistic vectors to a space which is composed of the m basic unit vectors of the m-dimensional Euclidean space and m(m-1)/spl middot//spl middot//spl middot/(m-j+1)/j! m-dimensional probabilistic vectors in which some components of each probabilistic vector are zero and the remainder are identical. Thus, the dynamic mapping maps the domain space with an infinite number of states to the mapping space which has a finite number of states. The proposed mapping provides an effective classification or cluster scheme when the features of the considered object or data are described by a probabilistic vector.
Starting Page	1450
Ending Page	1455
File Size	429715
Page Count	6
File Format	PDF
ISBN	0780355296
ISSN	10987576
DOI	10.1109/IJCNN.1999.831179
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-07-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nonlinear equations Mathematics Australia Dynamic range Hopfield neural networks Associative memory Neural networks
Content Type	Text
Resource Type	Article

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