Parameter learning in probabilistic databases: A least squares approach

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

Volumn 5211 LNAI, Issue PART 1, 2008, Pages 473-488

  Gutmann, Bernd ^a   Kimmig, Angelika ^a   Kersting, Kristian ^b   De Raedt, Luc ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b FRAUNHOFER IAIS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CURVE FITTING; EDUCATION; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; PROBABILITY; RANDOM PROCESSES; ROBOT LEARNING;

DO-MAINS; GAUSSIAN ERRORS; LEAST SQUARES; LOW APPROXIMATIONS; OPTIMIZATION PROBLEMS; PARAMETER LEARNINGS; PROBABILISTIC DATABASES; REAL WORLDS; TRAINING EXAMPLES;

DATABASE SYSTEMS;

EID: 56049106173     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87479-9_49     Document Type: Conference Paper

References (24)

1. Getoor, L., Taskar, B. (eds.): Statistical Relational Learning. MIT Press, Cambridge (2007)
2. De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S. (eds.): Probabilistic Inductive Logic Programming. LNCS (LNAI). vol. 4911, pp. 1-27. Springer, Heidelberg (2008)
3. Dalvi, N.N., Suciu, D.: Efficient query evaluation on probabilistic databases. In: Proceedings of VLDB, pp. 864-875 (2004)
4. De Raedt, L., Kimmig, A., Toivonen, H.: ProbLog: A probabilistic Prolog and its application in link discovery. In: Veloso, M. (ed.) IJCAI, pp. 2462-2467 (2007)
5. Cussens, J.: Parameter estimation in stochastic logic programs. MLJ 44(3), 245-271 (2001)
6. Sato, T., Kameya, Y.: Parameter learning of logic programs for symbolic-statistical modeling. J. Artif. Intell. Res. (JAIR) 15, 391-454 (2001)
7. Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: IJCAI, pp. 1300-1309 (1999)
8. Kersting, K., De Raedt, L.: Basic principles of learning bayesian logic programs. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S. (eds.) Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911, pp. 189-221. Springer, Heidelberg (2008)
9. Chen, J., Muggleton, S., Santos, J.: Learning probabilistic logic models from probabilistic examples (extended abstract). In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli. P. (eds.) ILP 2007. LNCS (LNAI). vol. 4894, pp. 22-23. Springer, Heidelberg (2008)
10. De Raedt, L., Kersting, K.: Probabilistic Logic Learning. ACM-SIGKDD Explorations: Special issue on Multi-Relational Data Mining 5(1), 31-48 (2003)
11. Charniak, E.: Tree-bank grammars. In: AAAI/IAAI, vol. 2, pp. 1031-1036 (1996)
12. De Raedt, L., Kersting, K., Torge, S.: Towards learning stochastic logic programs from proof-banks. In: AAA1, pp. 752-757 (2005)
13. Nottelmann, H., Fuhr, N.: Learning probabilistic datalog rules for information classification and transformation. In: CIKM. pp. 387-394. ACM, New York (2001)
14. Gupta, R., Sarawagi, S.: Creating probabilistic databases from information extraction models. In: VLDB, pp. 965-976 (2006)
15. De Raedt, L., Kersting, K., Kimmig, A., Revoredo, K., Toivonen, H.: Compressing probabilistic prolog programs. Machine Learning 70(2-3), 151-168 (2008)
16. Wrobel, S., Wettschereck, D., Sommer, E., Emde, W.: Extensibility in data mining systems. In: KDD, pp. 214-219 (1996)
17. Kimmig, A., De Raedt, L., Toivonen, H.: Probabilistic explanation based learning. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 176-187. Springer, Heidelberg (2007)
18. Lloyd, J.W.: Foundations of Logic Programming, 2nd edn. Springer, Berlin (1989)
19. Bryant. R.E.: Graph-based algorithms for boolean function manipulation. IEEE Trans. Computers 35(8), 677-691 (1986)
20. Poole, D.: Logic programming, abduction and probability - a top-down anytime algorithm for estimating prior and posterior probabilities. New Generation Comput, 11(3), 377-400 (1993)
21. De Raedt, L., Kersting, K.: Probabilistic inductive logic programming. In: BenDavid, S., Case, J., Maruoka, A. (eds.) ALT 2004. LNCS (LNAI). vol. 3244, pp. 19-36. Springer, Heidelberg (2004)
22. Mitchell, T.M.: Machine Learnning. McGraw-Hill, New York (1997)
23. Saul, L., Jaakkola, T., Jordan, M.: Mean field theory for sigmoid belief networks. JAIR 4, 61-76 (1996)
24. Sevon, P., Eronen, L., Hintsanen, P., Kulovesi, K., Toivonen, H.: Link discovery in graphs derived from biological databases. In: Leser, U., Naumann, F., Eckman, B. (eds.) DILS 2006. LNCS (LNBI), vol. 4075, pp. 35-49. Springer, Heidelberg (2006)