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