Learning Function-Free Horn Expressions View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1999-12

AUTHORS

Roni Khardon

ABSTRACT

The problem of learning universally quantified function free first order Horn expressions is studied. Several models of learning from equivalence and membership queries are considered, including the model where interpretations are examples (Learning from Interpretations), the model where clauses are examples (Learning from Entailment), models where extensional or intentional background knowledge is given to the learner (as done in Inductive Logic Programming), and the model where the reasoning performance of the learner rather than identification is of interest (Learning to Reason). We present learning algorithms for all these tasks for the class of universally quantified function free Horn expressions. The algorithms are polynomial in the number of predicate symbols in the language and the number of clauses in the target Horn expression but exponential in the arity of predicates and the number of universally quantified variables. We also provide lower bounds for these tasks by way of characterising the VC-dimension of this class of expressions. The exponential dependence on the number of variables is the main gap between the lower and upper bounds. More... »

PAGES

241-275

References to SciGraph publications

  • 1997. A logical framework for graph theoretical decision tree learning in INDUCTIVE LOGIC PROGRAMMING
  • 1999-04. Learning to Take Actions in MACHINE LEARNING
  • 1999. Learning Range Restricted Horn Expressions in COMPUTATIONAL LEARNING THEORY
  • 1995-06. On the learnability of disjunctive normal form formulas in MACHINE LEARNING
  • 1989-10. Learning conjunctive concepts in structural domains in MACHINE LEARNING
  • 1997. Learning Horn definitions with equivalence and membership queries in INDUCTIVE LOGIC PROGRAMMING
  • 1996-11. Classic learning in MACHINE LEARNING
  • 1998. Learning first-order acyclic Horn programs from entailment in INDUCTIVE LOGIC PROGRAMMING
  • 1997. Learning acyclic first-order horn sentences from entailment in ALGORITHMIC LEARNING THEORY
  • 1988-04. Queries and concept learning in MACHINE LEARNING
  • 1992-07. Learning conjunctions of Horn clauses in MACHINE LEARNING
  • 2002-09-24. Learning from Entailment of Logic Programs with Local Variables in ALGORITHMIC LEARNING THEORY
  • 1992-07. Lower bound methods and separation results for on-line learning models in MACHINE LEARNING
  • Journal

    TITLE

    Machine Learning

    ISSUE

    3

    VOLUME

    37

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1023/a:1007610422992

    DOI

    http://dx.doi.org/10.1023/a:1007610422992

    DIMENSIONS

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