ITEM METADATA RECORD
Title: ON A NOVEL UNSUPERVISED COMPETITIVE LEARNING ALGORITHM FOR SCALAR QUANTIZATION
Authors: Van Hulle, Marc ×
MARTINEZ, D #
Issue Date: 1994
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Series Title: IEEE transactions on neural networks vol:5 issue:3 pages:498-501
Abstract: This letter presents a novel unsupervised competitive learning rule called the boundary adaptation rule (BAR), for scalar quantization. It is shown both mathematically and by simulations that BAR converges to equiprobable quantizations of univariate probability density functions and that, in this way, it outperforms other unsupervised competitive learning rules.
ISSN: 1045-9227
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