Optimized associative memories for feature selection

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Performance in most pattern classifiers is improved when redundant or irrelevant features are removed, however, this is mainly achieved by high demanding computational methods or successive classifiers construction. This paper shows how Associative Memories can be used to get a mask value which represents a subset of features that clearly identifies irrelevant or redundant information for classification purposes, therefore, classification accuracy is improved while significant computational costs in the learning phase are reduced. An optimal subset of features allows register size optimization, which contributes not only to significant power savings but to a smaller amount of synthesized logic, furthermore, improved hardware architectures are achieved due to functional units size reduction, as a result, it is possible to implement parallel and cascade schemes for pattern classifiers on the same ASIC.

Original languageEnglish
Title of host publicationPattern Recognition and Image Analysis - Third Iberian Conference, IbPRIA 2007, Proceedings
PublisherSpringer Verlag
Pages435-442
Number of pages8
EditionPART 1
ISBN (Print)9783540728467
DOIs
StatePublished - 2007
Event3rd Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2007 - Girona, Spain
Duration: 6 Jun 20078 Jun 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4477 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2007
Country/TerritorySpain
CityGirona
Period6/06/078/06/07

Keywords

  • Feature selection
  • Masking techniques
  • Pattern classifier
  • Supervised learning

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