ITEM METADATA RECORD
Title: ON AN UNSUPERVISED LEARNING RULE FOR SCALAR QUANTIZATION FOLLOWING THE MAXIMUM-ENTROPY PRINCIPLE
Authors: Van Hulle, Marc ×
MARTINEZ, D #
Issue Date: 1993
Publisher: MIT PRESS
Series Title: Neural computation vol:5 issue:6 pages:939-953
Conference: date:MIT,DEPT BRAIN & COGNIT SCI,CAMBRIDGE,MA 02139
Abstract: A novel unsupervised learning rule, called Boundary Adaptation Rule (BAR), is introduced for scalar quantization. It is shown that the rule maximizes information-theoretic entropy and thus yields equiprobable quantizations of univariate probability density functions. It is shown by simulations that BAR outperforms other unsupervised competitive learning rules in generating equiprobable quantizations. It is also shown that our rule can do better or worse than the Lloyd I algorithm in minimizing average mean square error, depending on the input distribution. Finally, an application to adaptive non-uniform analog to digital (A/D) conversion is considered.
ISSN: 0899-7667
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Group Neurophysiology
× corresponding author
# (joint) last author

Files in This Item:

There are no files associated with this item.

Request a copy

 




All items in Lirias are protected by copyright, with all rights reserved.

© Web of science