Constructive learning with continuous-valued attributes

  • F. Bergadano
  • R. Bisio
Knowledge Acquisition And Machine Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 313)


In this paper we will present a methodology for dealing with continuous-valued attributes in constructive Concept Learning. This technique allows the system to use partially defined predicates, and set automatically the values of their parameters. In this way the user can define more easily the concept description language, and the search for discriminant expressions can be more effective. The method we propose is also a way of integrating statistical and symbolic approaches to Machine Learning, by using a description language based on first order logic, and an induction method which is also able to deal with numerical data. Although many related research issues still need to be investigated, this technique can be useful, and an example of a Concept Acquisition problem is given, where the proposed methodology is important, in order to obtain an acceptable solution.


Membership Function Order Logic Constructive Learning Concept Description Distribution Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • F. Bergadano
    • 1
  • R. Bisio
    • 2
  1. 1.Dipartimento di InformaticaUniversita' di TorinoTorinoItaly
  2. 2.C.S.E.L.T.TorinoItaly

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