A dynamic mapping based on probabilistic relaxation

Author

Fu, Alan M N ; Yan, Hong

Author_Institution

Sch. of Electr. & Inf. Eng., Sydney Univ., NSW, Australia

Volume

2

fYear

1999

fDate

36342

Firstpage

1450

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)···(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

Keywords

iterative methods; neural nets; pattern classification; probability; relaxation theory; Euclidean space; data clustering; domain space; dynamic mapping; iterative method; pattern classification; probabilistic relaxation; probabilistic vectors; Associative memory; Australia; Dynamic range; Hopfield neural networks; Mathematics; Neural networks; Nonlinear equations;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.831179

Filename

831179