Identifying Predictive Structures in Relational Data Using Multiple Instance Learning

Proceedings, Twentieth International Conference on Machine Learning

Volumn 2, Issue , 2003, Pages 528-535

  McGovern, Amy ^a   Jensen, David ^a  

^a Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; DATABASE SYSTEMS; IMAGE PROCESSING; INTERNET;

DIVERSE DENSITY METHOD; INTERNET MOVIE DATABASE (IMD); MULTIPLE INSTANCE LEARNING (MIL); MULTIPLE-INSTANCE (MI) TECHNIQUES;

LEARNING SYSTEMS;

EID: 1942484841     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Amar, R. A., Dooly, D. R., Goldman, S. A., & Zhang, Q. (2001). Multiple-instance learning of real-valued data. proc. of the 18th International Conference on Machine Learning (pp. 3-10). Morgan Kaufmann, SF, CA.
2. Blau, H., Immerman, N., & Jensen, D. (2002). A visual language for querying and updating graphs (Technical Report 2002-037). University of Massachusetts Amherst.
3. Bunke, H., & Shearer, K. (1998). A graph distance metric based on the maximal common subgraph. Pattern Recognition Letters, 19, 255-259.
4. Dietterich, T. G., Lathrop, R. H., & Lozano-Perez, T. (1997). Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence, 89, 31-71.
5. Goldman, S., Zhang, Q., Yu, W., & Fritts, J. E. (2002). Content-based image retrieval using multiple-instance learning. Proc. of the 19th International Conference on Machine Learning (pp. 682-689). Morgan Kaufmann, SF, CA.
6. Maron, O. (1998). Learning from ambiguity. Doctoral dissertation, Massachusetts Institute of Technology.
7. Maron, O., & Lozano-Pérez, T. (1998). A framework for multiple-instance learning. Advances in Neural Information Processing Systems 10 (pp. 570-576). Cambridge, Massachusetts: MIT Press.
8. Maron, O., & Ratan, A. L. (1998). Multiple-instance learning for natural scene classification. Proc. of the 15th International Conference on Machine Learning (pp. 341-349). Morgan Kaufmann, San Francisco, CA.
9. McGovern, A., & Jensen, D. (2003). Chi-squared: A simpler evaluation function for multiple-instance learning (Technical Report 03-14). Computer Science Department, University of Massachusetts Amherst.
10. Neville, J., Jensen, D., Friedland, L., & Hay, M. (2003). Learning relational probability trees. To appear in the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
11. Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, California: Morgan Kaufmann Publishers.
12. Zhang, Q., & Goldman, S. A. (2002). EM-DD: An improved multiple-instance learning technique. Advances in Neural Information Processing Systems 14. Cambridge, MA: MIT Press.
13. Zucker, J.-D., & Chevaleyre, Y. (2000). Solving multiple-instance and multiple-part learning problems with decision trees and decision rules. application to the mutagenesis problem (Technical Report 6). University of Paris.