Michèle Sebag, Céline Rouveirol  Home/Search
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Abstract: Learning in first-order logic (FOL) languages suffers from a specific difficulty: both induction and classification are potentially exponential in the size of hypotheses. This difficulty is usually dealt with by limiting the size of hypotheses, via either syntactic restrictions or search strategies. This paper is concerned with polynomial induction and use of FOL hypotheses with no size restrictions. This is done via stochastic matching: instead of exhaustively exploring the set of matchings... (Update)
Context of citations to this paper: More
...with the variable X instantiated. k local) Horn clauses. Other papers can be found in the area of stochastic evaluation [Sebag and Rouveirol, 1997; Srinivasan, 1999] The idea there is that one requires a hypothesis to be true (or false) with a high probability through the...
...experiments carded out using Progol in (Srinivasan et al. 1996) show an average accuracy of 83 . Two recent approaches, (Sebag and Rouveirol, 1997) and (Kramer and De Raedt, 2001) have reported a maximum accuracy of 93.6 and 94.7 , respectively for mutagenesis database. The...
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BibTeX entry: (Update)
Automatica, 14:465--471. Sebag, M. (1997). Tractable induction and classification in first order logic. In 15th International Joint Conference on Artificial Intelligence, Tokyo, Japan. http://citeseer.ist.psu.edu/5568.html More
@inproceedings{ sebag97tractable,
author = "M. Sebag and C. Rouveirol",
title = "Tractable induction and classification in first-order logic via stochastic matching",
booktitle = "Proceedings of the 15th International Joint Conference on Artificial Intelligence",
publisher = "Morgan Kaufmann",
pages = "888--893",
year = "1997",
url = "citeseer.ist.psu.edu/5568.html" }
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392 A theory and methodology of inductive learning (context) - Michalski - 1983 ACM DBLP
313 Inductive logic programming: Theory and methods - Muggleton, De Raedt - 1994 DBLP
274 Generalization as search (context) - Mitchell - 1982 DBLP
212 Inductive Logic Programming: Techniques and Applications (context) - Lavrac, Dzeroski - 1994
149 Quantifying inductive bias : AI learning algorithms and Vali.. (context) - Haussler - 1988
64 Relational instance based learning - Emde, Wettscherek - 1996
40 Relating chemical activity to structure: an examination of I.. - King, Srinivasan et al. - 1995
39 Learning in FOL with a similarity measure (context) - Bisson - 1992 DBLP
30 Further experimental evidence against the utility of Occam's.. - Webb - 1996
21 Guiding induction with domain theories (context) - Bergadano, Giordana - 1990
17 The role of prior knowledge in inductive learning (context) - Pazzani, Kibler - 1992
15 Delaying the choice of bias: A disjunctive version space app.. - Sebag - 1996 DBLP
8 Comparing the use of background knowledge by two ILP systems (context) - Srinivasan, Muggleton - 1995
6 Representation changes for efficient learning in structural .. - Zucker, Ganascia - 1996 DBLP
4 The need for bias in learning generalizations (context) - Mitchell - 1991
2 Search internsive concept induction (context) - Giordana, Neri - 1994
2 An efficient subsumption algorithm for ILP (context) - Kietz, Lubbe - 1994
1 First Order Regression (context) - is, say et al. - 1995 ACM DBLP
1 Constraint inductive logic programming (context) - Learning, Sebag et al. - 1996
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