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SPIN Learning and Forgetting Surface Classifications with Dynamic Neural Networks

  • This paper refers to the problem of adaptability over an infinite period of time, regarding dynamic networks. A never ending flow of examples have to be clustered, based on a distance measure. The developed model is based on the self-organizing feature maps of Kohonen [6], [7] and some adaptations by Fritzke [3]. The problem of dynamic surface classification is embedded in the SPIN project, where sub-symbolic abstractions, based on a 3-d scanned environment is being done.

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Metadaten
Author:Uwe R. Zimmer, Ewald von Puttkamer, Herman Keuchel
URN (permanent link):urn:nbn:de:hbz:386-kluedo-3006
Document Type:Preprint
Language of publication:English
Year of Completion:1993
Year of Publication:1993
Publishing Institute:Technische Universität Kaiserslautern
Date of the Publication (Server):2000/04/03
Source:ICANN 93, Amsterdam
Faculties / Organisational entities:Fachbereich Informatik
DDC-Cassification:0 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Licence (German):Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011