Basic principles of learning bayesian logic programs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4911 LNAI, Issue , 2008, Pages 189-221

  Kersting, Kristian ^a   De Raedt, Luc ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

BASIC (PROGRAMMING LANGUAGE); CLUSTER ANALYSIS; COMPUTER NETWORKS; COMPUTER PROGRAMMING; DISTRIBUTED PARAMETER NETWORKS; DYNAMIC PROGRAMMING; EDUCATION; FUZZY LOGIC; INFERENCE ENGINES; INTELLIGENT NETWORKS; KETONES; LOGIC PROGRAMMING; METROPOLITAN AREA NETWORKS; NETWORK PROTOCOLS; PROBABILITY; RANDOM VARIABLES; SET THEORY; SPEECH ANALYSIS; VARIATIONAL TECHNIQUES;

(E ,2E) THEORY; (U ,V) OPERATOR; BASIC PRINCIPLES; BAYESIAN; BAYESIAN LOGIC PROGRAMS (BLP); GENERAL (CO); HEIDELBERG (CO); IN ORDER; INDUCTIVE LOGIC PROGRAMMING (ILP); LEARNING BAYESIAN NETWORK (GLA); LOGIC PROGRAMS; NATURAL FRAMEWORK; PROBABILISTIC INDUCTIVE LOGIC PROGRAMMING; SPRINGER (CO);

BAYESIAN NETWORKS;

EID: 40249111606     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-78652-8_7     Document Type: Article

References (47)

1. Bangsø, O., Langseth, H., Nielsen, T.D.: Structural learning in object oriented domains. In: Russell, I., Kolen, J. (eds.) Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2001), Key West, Florida, USA, pp. 340-344. AAAI Press, Menlo Park (2001)
2. Bauer, H.: Wahrscheinlichkeitstheorie, 4th edn., Walter de Gruyter, Berlin, New York (1991)
3. Binder, J., Koller, D., Russell, S., Kanazawa, K.: Adaptive Probabilistic Networks with Hidden Variables. Machine Learning 29(2-3), 213-244 (1997)
4. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
5. Blockeel, H., De Raedt, L.: Lookahead and discretization in ilp. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 77-85. Springer, Heidelberg (1997)
6. Blockeel, H., De Raedt, L.: ISIDD: An Interactive System for Inductive Database Design. Applied Artificial Intelligence 12(5), 385 (1998)
7. Buntine, W.: A guide to the literature on learning probabilistic networks from data. IEEE Transaction on Knowledge and Data Engineering 8, 195-210 (1996)
8. Cheng, J., Hatzis, C., Krogel, M.-A., Morishita, S., Page, D., Sese, J.: KDD Cup 2002 Report. SIGKDD Explorations 3(2), 47-64 (2002)
9. Cooper, G.F.: The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence 42, 393-405 (1990)
10. Cowell, R.G., Dawid, A.P., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic networks and expert systems. In: Statistics for engineering and information, Springer, Heidelberg (1999)
11. Cussens, J.: Parameter estimation in stochastic logic programs. Machine Learning 44(3), 245-271 (2001)
12. De Raedt, L.: Logical settings for concept-learning. Artificial Intelligence 95(1), 197-201 (1997)
13. De Raedt, L., Bruynooghe, M.: A theory of clausal discovery. In: Bajcsy, R. (ed.) Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI 1993), Chambery, France, pp. 1058-1063. Morgan Kaufmann, San Francisco (1993)
14. De Raedt, L., Dehaspe, L.: Clausal discovery. Machine Learning 26(2-3), 99-146 (1997)
15. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. B 39, 1-39 (1977)
16. Dick, U., Kersting, K.: Fisher Kernels for relational data. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) Proceedings of the 17th European Conference on Machine Learning (ECML 2006), Berlin, Germany, pp. 112-125 (2006)
17. Flach, P.A., Lachiche, N.: 1BC: A first-order Bayesian classifier. In: Džeroski, S., Flach, P.A. (eds.) ILP 1999. LNCS (LNAI), vol. 1634, pp. 92-103. Springer, Heidelberg (1999)
18. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131-163 (1997)
19. Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Dean, T. (ed.) Proceedings of the Sixteenth International Joint Conferences on Artificial Intelligence (IJCAI 1999), Stockholm, Sweden, pp. 1300-1309. Morgan Kaufmann, San Francisco (1999)
20. Getoor, L., Koller, D., Taskar, B., Friedman, N.: Learning probabilistic relational models with structural uncertainty. In: Getoor, L., Jensen, D. (eds.) Proceedings of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data, AAAI Press, Menlo Park (2000)
21. Gilks, W.R., Thomas, A., Spiegelhalter, D.J.: A language and program for complex bayesian modelling. The Statistician 43 (1994)
22. Heckerman, D.: A Tutorial on Learning with Bayesian Networks. Technical Report MSR-TR-95-06, Microsoft Research (1995)
23. Heckerman, D., Breese, J.: Causal Independence for Probability Assessment and Inference Using Bayesian Networks. Technical Report MSR-TR-94-08, Microsoft Research (1994)
24. Jaeger, M.: Relational Bayesian networks. In: Geiger, D., Shenoy, P.P. (eds.) Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI 1997), Providence, Rhode Island, USA, pp. 266-273. Morgan Kaufmann, San Francisco (1997)
25. Jamshidian, M., Jennrich, R.I.: Accleration of the EM Algorithm by using QuasiNewton Methods. Journal of the Royal Statistical Society B 59(3), 569-587 (1997)
26. Jensen, F.V.: Gradient descent training of bayesian networks. In: Hunter, A., Parsons, S. (eds.) ECSQARU 1999. LNCS (LNAI), vol. 1638, pp. 190-200. Springer, Heidelberg (1999)
27. Jensen, F. V.: Bayesian networks and decision graphs. Springer, Heidelberg (2001)
28. Kersting, K., De Raedt, L.: Bayesian logic programs. In: Cussens, J., Frisch, A. (eds.) Work-in-Progress Reports of the Tenth International Conference on Inductive Logic Programming (ILP 2000) (2000), http://www.SunSITE. Informatik.RWTH-Aachen.DE/Publications/CEUR-WS/Vol-35/
29. Kersting, K., De Raedt, L.: Bayesian logic programs. Technical Report 151, University of Freiburg, Institute for Computer Science (submitted) (April 2001)
30. Kersting, K., De Raedt, L., Kramer, S.: Interpreting Bayesian Logic Programs. In: Getoor, L., Jensen, D. (eds.) Working Notes of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data (SRL), Austin, Texas, AAAI Press, Menlo Park (2000)
31. Kersting, K., Gartner, T.: Fisher Kernels for Logical Sequences. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, p. 205. Springer, Heidelberg (2004)
32. Koller, D.: Probabilistic relational models. In: Džeroski, S., Flach, P.A. (eds.) ILP 1999. LNCS (LNAI), vol. 1634, pp. 3-13. Springer, Heidelberg (1999)
33. Koller, D., Pfeffer, A.: Learning probabilities for noisy first-order rules. In: Proceedings of the Fifteenth Joint Conference on Artificial Intelligence (IJCAI 1997), Nagoya, Japan, pp. 1316-1321 (1997)
34. Lam, W., Bacchus, F.: Learning Bayesian belief networks: An approach based on the MDL principle. Computational Intelligence 10(4) (1994)
35. Langseth, H., Bangsø, O.: Parameter learning in object oriented Bayesian networks. Annals of Mathematics and Artificial Intelligence 32(1-2), 221-243 (2001)
36. Lauritzen, S.L.: The EM algorithm for graphical association models with missing data. Computational Statistics and Data Analysis 19, 191-201 (1995)
37. Lloyd, J.W.: Foundations of Logic Programming, 2nd edn. Springer, Berlin (1989)
38. McKachlan, G.J., Krishnan, T.: The EM Algorithm and Extensions. John Eiley & Sons, Inc. (1997)
39. Muggleton, S.H.: Learning stochastic logic programs. In: Getoor, L., Jensen, D. (eds.) Working Notes of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data (SRL), Austin, Texas, AAAI Press, Menlo Park (2000)
40. Ngo, L., Haddawy, P.: Answering queries from context-sensitive probabilistic knowledge bases. Theoretical Computer Science 171, 147-177 (1997)
41. Pearl, J.: Reasoning in Intelligent Systems: Networks of Plausible Inference, 2nd edn. Morgan Kaufmann, San Francisco (1991)
42. Poole, D.: Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence 64, 81-129 (1993)
43. Sato, T., Kameya, Y.: Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research 15, 391-454 (2001)
44. Srinivasan, A., Muggleton, S., Bain, M.: The justification of logical theories based on data compression. In: Furukawa, K., Michie, D., Muggleton, S. (eds.) Machine Intelligence, vol. 13, Oxford University Press, Oxford (1994)
45. Sterling, L., Shapiro, E.: The Art of Prolog: Advanced Programming Techniques. MIT Press, Cambridge (1986)
46. Taskar, B., Segal, E., Koller, D.: Probabilistic clustering in relational data. In: Nebel, B. (ed.) Seventeenth International Joint Conference on Artificial Intelligence (IJCAI 2001), Seattle, Washington, USA, pp. 870-887. Morgan Kaufmann, San Francisco (2001)
47. Xiang, Y., Wong, S.K.M., Cereone, N.: Critical remarks on single link search in learning belief networks. In: Horvitz, E., Jensen, F.V. (eds.) Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence (UAI 1996), Portland, Oregon, USA, pp. 564-571. Morgan Kaufmann, San Francisco (1996)