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Learning in dynamically changing domains: Theory revision and context dependence issues

  • Charles Taylor
  • Gholamreza Nakhaeizadeh
Part III: Workshop Position Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1224)

Abstract

Dealing with dynamically changing domains is a very important topic in Machine Learning (ML) which has very interesting practical applications. Some attempts have already been made both in the statistical and machine learning communities to address some of the issues. In this paper we give a state of the art from the available literature in this area. We argue that a lot of further research is still needed, outline the directions that such research should go and describe the expected results. We argue also that most of the problems in this area can be solved only by interaction between the researchers of both the statistical and ML-communities.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Charles Taylor
    • 1
  • Gholamreza Nakhaeizadeh
    • 2
  1. 1.University of LeedsLeedsUK
  2. 2.Daimler-Benz Research and TechnologyUlmGermany

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