Opposition-based learning: A new scheme for machine intelligence

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

Abstract

Opposition-based learning as a new scheme for machine intelligence is introduced. Estimates and counter-estimates, weights and opposite weights, and actions versus counter-actions are the foundation of this new approach. Examples are provided. Possibilities for extensions of existing learning algorithms are discussed. Preliminary results are provided.

Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet
Pages695-701
Number of pages7
StatePublished - 2005
EventInternational Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IAWTIC 2005 - Vienna, Austria
Duration: Nov 28 2005Nov 30 2005

Publication series

NameProceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet
Volume1

Conference

ConferenceInternational Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IAWTIC 2005
Country/TerritoryAustria
CityVienna
Period11/28/0511/30/05

ASJC Scopus subject areas

  • Engineering(all)

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