(Enter summary)
Abstract: . Induction in first-order logic languages suffers from an additional
factor of complexity compared to induction in attribute-value
languages: the number of possible matchings between a candidate hypothesis
and a training example.
This paper investigates the use of a stochastic bias to control this factor
of complexity: the exhaustive exploration of the matching space is replaced
by considering a fixed number of matchings, obtained by random
sampling. One thereby constructs from positive and... (Update)
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BibTeX entry: (Update)
@inproceedings{ sebag96polynomialtime,
author = "Michele Sebag and Celine Rouveirol",
title = "Polynomial-Time Learning in Logic Programming and Constraint Logic Programming",
booktitle = "Inductive Logic Programming Workshop",
pages = "105-126",
year = "1996",
url = "citeseer.ist.psu.edu/161576.html" }
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