A modified spreading algorithm for autoassociation in weightless neural networks

Christopher Browne, Joel de L. Pereira Castro Jr., Amos Storkey

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper describes a problem with the conventional Hamming metric spreading algorithm often employed in weightless neural networks. The algorithm can cause incorrect classifications in some cases where a section of the neuron input vector is noise. The conditions under which such error occurs are described and a modified spreading algorithm proposed to overcome this problem has its validity demonstrated theoretically and tested in two practical applications.
Original languageEnglish
Title of host publicationArtificial Neural Networks — ICANN 96
Subtitle of host publication1996 International Conference Bochum, Germany, July 16–19, 1996 Proceedings
EditorsChristoph von der Malsburg, Werner von Seelen, Jan C. Vorbrüggen, Bernhard Sendhoff
PublisherSpringer Berlin Heidelberg
Number of pages6
ISBN (Electronic)978-3-540-68684-2
ISBN (Print)978-3-540-61510-1
Publication statusPublished - 1996

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Dive into the research topics of 'A modified spreading algorithm for autoassociation in weightless neural networks'. Together they form a unique fingerprint.

Cite this