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Successive Recognition using Local State Models
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-5698-5983
2002 (English)In: Proceedings SSAB02 Symposium on Image Analysis: Lund, 2002, 9-12 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes how a world model for successive recognition can be learned using associative learning. The learned world model consists of a linear mapping that successively updates a high-dimensional system state using performed actions and observed percepts. The actions of the system are learned by rewarding actions that are good at resolving state ambiguities. As a demonstration, the system is used to resolve the localisation problem in a labyrinth.

Place, publisher, year, edition, pages
2002. 9-12 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-21652OAI: oai:DiVA.org:liu-21652DiVA: diva2:245959
Available from: 2009-10-08 Created: 2009-10-05 Last updated: 2015-12-10

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Forssen, Per-Erik

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf