View learning for statistical relational learning: With an application to mammography

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2005, Pages 677-683

  Davis, Jesse ^a   Burnside, Elizabeth ^a   Dutra, Inês ^a   Page, David ^a   Ramakrishnan, Raghu ^a   Costa, Vítor Santos ^a   Shavlik, Jude ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYES NET; DATABASE APPLICATIONS; DATABASE SCHEMAS; PROBABILISTIC MODELS; RELATIONAL DATABASE; RELATIONAL TABLES; STATISTICAL RELATIONAL LEARNING;

ARTIFICIAL INTELLIGENCE;

DATABASE SYSTEMS;

EID: 84880739463     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. M.L. Brown, F. Houn, E.A. Sickles, and L.G. Kessler. Screening mammography in community practice: positive predictive value of abnormal findings and yield of follow-up diagnostic procedures. AJR Am J Roentgenol, 165:1373-1377, 1995.
2. E.S. Burnside, D.L. Rubin, and R.D. Shachter. A Bayesian network for screening mammography. In AMIA, pages 106-110, 2000.
3. E.S. Burnside, D.L. Rubin, and R.D. Shachter. Using a Bayesian network to predict the probability and type of breast cancer represented by microcalcifications on mammography. Medinfo, 2004:13-17, 2004.
4. V. Santos Costa, D. Page, M. Qazi, and J. Cussens. CLP(BN): Constraint logic programming for probabilistic knowledge. In UAI-03, pages 517-524, Acapulco, 2003.
5. J. Cussens. Parameter estimation in stochastic logic programs. Machine Learning, 44(3):245-271, 2001. (Pubitemid 32761112)
6. G. Ecklund. Shortage of qualified breast imagers could lead to crisis. Diagn Imaging, 22:31-33, 2000.
7. Nir Friedman, David Geiger, and Moises Goldszmidt. Bayesian networks classifiers. Machine Learning, 29:131-163, 1997.
8. Nir Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In Proceedings of the 16th International Joint Conference on Artificial Intelligence. Stockholm, Sweden, 1999.
9. Nir Friedman, I. Nachman, and D. Pe'er. Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm. In UAI-99, pages 206-215, San Francisco, CA, 1999. Morgan Kaufmann Publishers.
10. Dan Geiger. An entropy-based learning algorithm of Bayesian conditional trees. In UAI-92, pages 92-97, San Mateo, CA, 1992. Morgan Kaufmann Publishers.
11. D Heckerman, C Meek, and D Koller. Probabilistic Entity-Relationship Models, PRMs, and Plate Models, Technical Report MSR-TR-2004-30, Microsoft Research. Technical report, Microsoft Research, 2004.
12. C.E. Kahn Jr, L.M. Roberts, K.A. Shaffer, and P. Haddawy. Construction of a Bayesian network for mammographic diagnosis of breast cancer. Comput Biol Med., 27:19-29, 1997. (Pubitemid 27121972)
13. K. Kersting and L. De Raedt. Basic principles of learning Bayesian logic programs. Technical report, Institute for Computer Science, University of Freiburg, Germany, 2002.
14. Arno J. Knobbe, Marc de Haas, and Arno Siebes. Propositionalisation and aggregates. In PKDD01, pages 277-288, 2001.
15. S.H. Muggleton. Inductive Logic Programming. New Generation Computing, 8:295-318, 1991.
16. S.H. Muggleton. Learning stochastic logic programs. Electronic Transactions in Artificial Intelligence, 4(041), 2000.
17. Jennifer Neville, David Jensen, Lisa Friedland, and Michael Hay. Learning relational probability trees. In KDD '03, pages 625-630. ACM Press, 2003.
18. Claudia Perlich and Foster Provost. Aggregation-based feature invention and relational concept classes. In KDD '03, pages 167-176, 2003.
19. U. Pompe and I. Kononenko. Naive Bayesian classifier within ILP-R. In L. De Raedt, editor, ILP95, pages 417-436, 1995.
20. Alexandrin Popescul, Lyle H. Ungar, Steve Lawrence, and David M. Pennock. Statistical relational learning for document mining. In ICDM03, pages 275-282, 2003.
21. R. Ramakrishnan and J. Gehrke. Database Management Systems. McGraw Hill, 2000.
22. Matt Richardson and Pedro Domingos. Markov logic networks. http://www.cs.washington.edu/homes/pedrod/kbmn.pdf, 2004.
23. T. Sato. A statistical learning method for logic programs with distributional semantics. In L. Sterling, editor, Proceedings of the Twelth International conference on logic programming, pages 715-729, Cambridge, Massachusetts, 1995. MIT Press.
24. E.A. Sickles, D.E. Wolverton, and K.E. Dee. Performance parameters for screening and diagnostic mammography: specialist and general radiologists. Radiology, 224:861-869, 2002. (Pubitemid 34919825)
25. A. Srinivasan and R. King. Feature construction with inductive logic programming: A study of quantitative predictions of biological activity aided by structural attributes. In ILP97, pages 89-104, 1997.
26. Ashwin Srinivasan. The Aleph Manual, 2001.
27. Celine Vens, Anneleen Van Assche, Hendrik Blockeel, and Sašo Džeroski. First order random forests with complex aggregates. In ILP, pages 323-340, 2004.