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Abstract: This paper presents an efficient approach to address the task of learning from large number of learning examples in structural domains. While in attribute-value representations only one mapping is possible between descriptions, in first order logic representations there are potentially many mappings. Classic approaches consider all mappings and then define a restricted hypothesis space to cope with the intractability of exploring all mappings. Our approach is to select one particular... (Update)
Context of citations to this paper: More
.... 1992) KBG (Bisson, 1992) REGAL (Giordana Neri, 1994) PROGOL (Muggleton, 1995) RIBL (Emde Wettscherek, 1996) and REMO (Zucker Ganascia, 1996) to name a few. The specific difficulty of ILP is related to the matching step: If a clause involves three literals part and...
...program for translating the trains problem of the East West challenge into a propositional representation. Zucker and Ganascia [ Zucker and Ganascia, 1996 ] proposed to decompose structured examples into several learning examples, which are descriptions of parts of what they...
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BibTeX entry: (Update)
J.-D. Zucker and J.-G. Ganascia. Representation changes for efficient learning in structural domains. In Saitta, L., ed., Proceedings of the 13 th International Conference on Machine Learning, 543--551. Morgan Kaufmann, 1996. http://citeseer.ist.psu.edu/zucker96representation.html More
@inproceedings{ zucker96representation,
author = "Jean-Daniel Zucker and Jean-Gabriel Ganascia",
title = "Representation Changes for Efficient Learning in Structural Domains",
booktitle = "International Conference on Machine Learning",
pages = "543-551",
year = "1996",
url = "citeseer.ist.psu.edu/zucker96representation.html" }
Citations (may not include all citations):
The graph only includes citing articles where the year of publication is known.
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