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Can Relational Learning Scale Up?

  • Attilio Giordana
  • Lorenza Saitta
  • Michele Sebag
  • Marco Botta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1932)

Abstract

A key step of supervised learning is testing whether a can- didate hypothesis covers a given example. When learning in first order logic languages, the covering test is equivalent to a Constraint Satisfaction Problem (CSP). For critical values of some order parameters, CSPs present a phase pransition, that is, the probability of finding a solution abruptly drops from almost 1 to almost 0, and the complexity drama- tically increases. This paper analyzes the complexity and feasibility of learning in first order logic languages with respect to the phase transition of the covering test.

Keywords

Phase Transition Predictive Accuracy Information Gain Hard Problem Constraint Satisfaction Problem 
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 2000

Authors and Affiliations

  • Attilio Giordana
    • 1
  • Lorenza Saitta
    • 1
  • Michele Sebag
    • 2
  • Marco Botta
    • 3
  1. 1.DISTAUniversità del Piemonte OrientaleAlessandriaItaly
  2. 2.École PolytechniqueLMSPalaiseauFrance
  3. 3.Dipartimento di InformaticaUniversità di TorinoTorinoItaly

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