Fuzzifying Hyperplanes in the Hypothesis Space

  • Martin Eineborg
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 14)


This paper is concerned with the assumption made by inductive logic programming, that the relations in an induced hypothesis must have a precise definition. In this paper the rules of a hypothesis are made fuzzy by applying a method, originally intended for classifying uncovered examples, called rule stretching when classifying new examples. A number of experiments were performed where hypotheses were induced using the inductive logic programming system Virtual Predict. The rule stretching method was compared with classifying test examples using the most accurate rule. The encouraging result of the experiments showed that rule stretching outperforms the most accurate rule in six out of seven experiments.


Fuzzy Logic Inductive Logic Inductive Logic Programming Student Loan Hypothesis Space 
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 2002

Authors and Affiliations

  • Martin Eineborg
    • 1
  1. 1.Machine Learning Group, Department of Computer and Systems SciencesStockholm University/Royal Institute of TechnologyStockholmSweden

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