Effective learning in dynamic environments by explicit context tracking View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1993

AUTHORS

Gerhard Widmer , Miroslav Kubat

ABSTRACT

Daily experience shows that in the real world, the meaning of many concepts heavily depends on some implicit context, and changes in that context can cause radical changes in the concepts. This paper introduces a method for incremental concept learning in dynamic environments where the target concepts may be context-dependent and may change drastically over time. The method has been implemented in a system called FLORA3. FLORA3 is very flexible in adapting to changes in the target concepts and tracking concept drift. Moreover, by explicitly storing old hypotheses and re-using them to bias learning in new contexts, it possesses the ability to utilize experience from previous learning. This greatly increases the system's effectiveness in environments where contexts can reoccur periodically. The paper describes the various algorithms that constitute the method and reports on several experiments that demonstrate the flexibility of FLORA3 in dynamic environments. More... »

PAGES

227-243

References to SciGraph publications

Book

TITLE

Machine Learning: ECML-93

ISBN

978-3-540-56602-1
978-3-540-47597-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-56602-3_139

DOI

http://dx.doi.org/10.1007/3-540-56602-3_139

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036804921


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