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Evaluation of learning systems : An artificial data-based approach

  • H. Lounis
  • G. Bisson
Part 8: Applications
Part of the Lecture Notes in Computer Science book series (LNCS, volume 482)

Abstract

Experimentation has an important role in determining the capacities and restrictions of machine learning (ML) systems. In this paper we present the definition of some sensitivity and evaluation criteria which can be used to perform an evaluation of learning systems. Moreover, in order to overcome some of the limitations of real data sets, we introduce the specification of a parametrable generator of artificial learning sets which allows us to make easily complete experiments to discover some empirical rules of behavior for ML algorithms. Finally, we give some results obtained with different algorithms, showing that artificial data bases approach is an interesting direction to explore.

Keywords

Evaluation of Machine Learning Algorithms Sensitivity Criteria Evaluation Criteria Artificial Data Base Parametrable Generator Modelization of Learning Domains 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • H. Lounis
    • 1
  • G. Bisson
    • 1
  1. 1.LRI, Equipe Inférence et ApprentissageUniversité Paris-SudOrsay CedexFrance

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