Learning logic programs by using the product homomorphism method

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback
Take a look at the new version of this page: [ beta version ]. Tell us what you think.

Learning logic programs by using the product homomorphism method

Full text

Pdf (1.58 MB)

Source

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

Bibliometrics

Downloads (6 Weeks): 0, Downloads (12 Months): 10, Citation Count: 4

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/267460.267468 What is a DOI?

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.

	1	W. W. Cohen. Pac-learning recursive logic programs: efficient algorithms. J. AI Research, 2:501-539, 1995.
	2	W. W. Cohen. Pac-learning recursive logic programs: ative results. J. AI Research, 2:541-573, 1995.
	3	William W. Cohen, The dual DFA learning problem (extended abstract): hardness results for programming by demonstration and learning first-order representations, Proceedings of the ninth annual conference on Computational learning theory, p.29-40, June 28-July 01, 1996, Desenzano del Garda, Italy [doi>10.1145/238061.238066]
	4	William W. Cohen, Pac-learning non-recursive Prolog clauses, Artificial Intelligence, v.79 n.1, p.1-38, Nov. 1995 [doi>10.1016/0004-3702(94)00034-4]
	5	William W. Cohen , Haym Hirsh, The Learnability of Description Logics with Equality Constraints, Machine Learning, v.17 n.2-3, p.169-199, Nov./Dec. 1994 [doi>10.1007/BF00993470]
	6	Luc De Raedt , Sašo Džeroski, First-order jk-clausal theories are PAC-learnable, Artificial Intelligence, v.70 n.1-2, p.375-392, Oct. 1994 [doi>10.1016/0004-3702(94)90112-0]
	7	Sašo Džeroski , Stephen Muggleton , Stuart Russell, PAC-learnability of determinate logic programs, Proceedings of the fifth annual workshop on Computational learning theory, p.128-135, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130399]
	8	Michael Frazier , Leonard Pitt, CLASSIC learning, Proceedings of the seventh annual conference on Computational learning theory, p.23-34, July 12-15, 1994, New Brunswick, New Jersey, United States [doi>10.1145/180139.174994]
	9	R. H~iggkvist, P. Hell, D. J. Miller, and V. Neumann Lara. On multiplicative graphs and the product conjecture. Combinatorica, 8:63-74, 1988.
	10	F. Harary. Graph Theory. Addison-Wesley, 1969.
	11	E Hell, J. Ne~effil, and X. Zhu. Duality and polynomial testing of tree homomorphisms. Trans. Am. Math. Soc., 348:1281-1297, 1996.
	12	Tamás Horváth , Robert H. Sloan , György Turán, Learning Logic Programs with Random Classification Noise, Selected Papers from the 6th International Workshop on Inductive Logic Programming, p.315-336, August 26-28, 1996
	13	T. Horv~tth and G. Tur~in. Learning logic programs with structured background knowledge. In 5th Int. Workshop on Inductive Logic Programming, pages 53-76, 1995. Also in Advances in Inductive Logic Programming (ed. L. De Raedt). IOS Press, 1996, pages 172-191. (IOS Frontiers in AI and Appl.).
	14	Jörg-Uwe Kietz, Some Lower Bounds for the Computational Complexity of Inductive Logic Programming, Proceedings of the European Conference on Machine Learning, p.115-123, April 05-07, 1993
	15	Jörg-Uwe Kietz , Sašo Džeroski, Inductive logic programming and learnability, ACM SIGART Bulletin, v.5 n.1, p.22-32, Jan. 1994 [doi>10.1145/181668.181674]
	16	Nada Lavrac , Saso Dzeroski, Inductive Logic Programming: Techniques and Applications, Routledge, New York, NY, 1993
	17	J. W. Lloyd, Foundations of logic programming, Springer-Verlag New York, Inc., New York, NY, 1984
	18	L. Lov,'isz. Combinatorial Problems and Exercises. North-Holland Publishing Company, 1979.
	19	W. Maass and G. Tur~in. On !earnability and predicate logic. In BISFAI '95 (Bar-llan Symposium on the Foundations of Artificial Intelligence), pages 75-85, 1995.
	20	S. Muggleton and C. Feng. Efficient induction of logic programs. In S. Muggleton, editor, Inductive Logic Programming, pages 281-298. Academic Press, 1992.
	21	S. Muggleton, R. King, and M. Sternberg. Protein secondary structure prediction using logic-based machine learning. Protein Engineering, 5(7):647-657, 1992.
	22	Daniel N. Osherson , Michael Stob , Scott Weinstein, New directions in automated scientific discovery, Information Sciences: an International Journal, 57-58, p.217-230, Sept./Dec. 1991
	23	C. D. Page and A. M. Frisch. Generalization and learnability: a study of constrained atoms. In S. Muggleton, editor, Inductive Logic Programming, pages 29- 61. Academic Press, 1992.
	24	G. D. Plotkin. Automatic Methods of Inductive Inference. PhD thesis, Edinburgh University, 1971.
	25	Pat Langley, Editorial: Advice to Machine Learning Authors, Machine Learning, v.5 n.3, p.233-237, Aug. 1990 [doi>10.1023/A:1022647305786]

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