Clustering with multilayer perceptrons and hebbian learning based on kullback-leibler divergence

Author

Filho, Jugurt R Montalvão ; Bezerra, Murilo A., Jr. ; Oliveira, Levi P.

Author_Institution

Lab. Automacao, Univ. Tiradentes, Aracaju

fYear

2004

fDate

Sept. 29 2004-Oct. 1 2004

Firstpage

243

Lastpage

252

Abstract

A new local (Hebbian) learning algorithm for artificial neurons is presented. It is shown that, in spite of its implementation simplicity, this new algorithm, applied to neurons with sigmoidal activation function, performs data clustering by finding valleys of the probability density function (PDF) of the multivariate random variables that model incoming data. Some interesting features of this new algorithm are presented and illustrated by practical experiments

Keywords

Hebbian learning; multilayer perceptrons; pattern clustering; Hebbian learning; Kullback-Leibler divergence; artificial neurons; data clustering; multilayer perceptrons; multivariate random variables; probability density function; sigmoidal activation function; Annealing; Clustering algorithms; Cost function; Hebbian theory; Multilayer perceptrons; Neural networks; Neurons; Probability density function; Prototypes; Random variables;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning for Signal Processing, 2004. Proceedings of the 2004 14th IEEE Signal Processing Society Workshop

Conference_Location

Sao Luis

ISSN

1551-2541

Print_ISBN

0-7803-8608-4

Type

conf

DOI

10.1109/MLSP.2004.1422980

Filename

1422980

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3077853