Learning logic programs by using the product homomorphism method

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Learning logic programs by using the product homomorphism method

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Annual Workshop on Computational Learning Theory archive
Proceedings of the tenth annual conference on Computational learning theory table of contents

Nashville, Tennessee, United States

Pages: 10 - 20

Year of Publication: 1997

ISBN:0-89791-891-6

Authors

Tamás Horváth	Dept. of Applied Informatics, József A. University, H-6720 Szeged, Hungary
Robert H. Sloan	Dept. of EE & Comp. Sci., U. Illinois at Chicago, 851 S. Morgan St. Rm 1120, Chicago, IL
György Turán	Dept. of Math., Stat., & Comp. Sci., U. Illinois at Chicago, Research Group on Artificial Intelligence, Hungarian Academy of Sciences

Sponsors

AT&T Labs :
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
Vanderbilt University : Vanderbilt University

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 0, Downloads (12 Months): 12, Citation Count: 4

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY 4

	Roni Khardon, Learning first order universal Horn expressions, Proceedings of the eleventh annual conference on Computational learning theory, p.154-165, July 24-26, 1998, Madison, Wisconsin, United States
	Irene Tsapara , György Turán, Learning atomic formulas with prescribed properties, Proceedings of the eleventh annual conference on Computational learning theory, p.166-174, July 24-26, 1998, Madison, Wisconsin, United States
	Roni Khardon, Learning Function-Free Horn Expressions, Machine Learning, v.37 n.3, p.241-275, Dec. 1999
	William W. Cohen, Hardness Results for Learning First-Order Representations and Programming by Demonstration, Machine Learning, v.30 n.1, p.57-87, Jan. 1998