Effective learning in dynamic environments by explicit context tracking

Gerhard Widmer, Miroslav Kubat

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

94 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationMachine Learning
Subtitle of host publicationECML 1993 - European Conference on Machine Learning, Proceedings
EditorsPavel B. Brazdil
PublisherSpringer Verlag
Number of pages17
ISBN (Print)9783540566021
StatePublished - 1993
Externally publishedYes
Event1st European Conference on Machine Learning, ECML 1993 - Vienna, Austria
Duration: Apr 5 1993Apr 7 1993

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume667 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other1st European Conference on Machine Learning, ECML 1993

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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