Multi-instance learning with any hypothesis class

Journal of Machine Learning Research

Volumn 13, Issue , 2012, Pages 2999-3039

  Sabato, Sivan ^a   Tishby, Naftali ^b  

^a MICROSOFT RESEARCH   (United States)

^b HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

Learning theory; Multiple instance learning; PAC learning; Sample complexity; Supervised classification

Indexed keywords

LEARNING THEORY; MULTIPLE-INSTANCE LEARNING; PAC LEARNING; SAMPLE COMPLEXITY; SUPERVISED CLASSIFICATION;

HEURISTIC ALGORITHMS; LEARNING ALGORITHMS;

SUPERVISED LEARNING;

EID: 84869481832     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (39)

1. N. Alon, S. Ben-David, N. Cesa-Bianchi, and D. Haussler. Scale-sensitive dimensions, uniform convergence, and learnability. In Foundations of Computer Science, 1993. Proceedings., 34th Annual Symposium on, pages 292-301. IEEE, 1993.
2. N. Alon, S. Ben-David, N. Cesa-Bianchi, and D. Haussler. Scale-sensitive dimensions, uniform convergence, and learnability. J. ACM, 44(4):615-631, 1997. (Pubitemid 127617707)
3. S. Andrews. Learning from Ambiguous Examples. PhD thesis, Brown University, May 2007.
4. S. Andrews and T. Hofmann. Multiple-instance learning via disjunctive programming boosting. In Advances in Neural Information Processing Systems 16, 2003.
5. S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-instance learning. In Advances in Neural Information Processing Systems 15, pages 561-568, 2002.
6. M. Anthony and P. L. Bartlett. Neural Network Learning: Theoretical Foundations. Cambridge University Press, 1999.
7. P. Auer, P. M. Long, and A. Srinivasan. Approximating hyper-rectangles: learning and pseudorandom sets. Journal of Computer and System Sciences, 57(3):376-388, 1998. (Pubitemid 128374556)
8. B. Babenko, N. Verma, P. Dollar, and S. Belongie. Multiple instance learning with manifold bags. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), ICML '11, pages 81-88, 2011.
9. P. L. Bartlett and S.Mendelson. Rademacher and Gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3:463-482, 2002.
10. P. L. Bartlett, S. R. Kulkarni, and S. E. Posner. Covering numbers for real-valued function classes. IEEE Transactions on Information Theory, 43(5):1721-1724, 1997. (Pubitemid 127826464)
11. A. Blum and A. Kalai. A note on learning from multiple-instance examples. Machine Learning, 30 (1):23-29, 1998. (Pubitemid 128690573)
12. C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273-297, September 1995.
13. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Ṕerez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89(1-2):31-71, 1997. (Pubitemid 127412230)
14. R. M. Dudley. Central limit theorems for empirical measures. The Annals of Probability, 6(6): 899-929, 1978.
15. R.M. Dudley. The sizes of compact subsets of hilbert space and continuity of gaussian processes. Journal of Functional Analysis, 1(3):290 -330, 1967.
16. Y. Freund and R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, August 1997. (Pubitemid 127433398)
17. T. Gärtner, P. A. Flach, A. Kowalczyk, and A. J. Smola. Multi-instance kernels. In Proceedings of the Nineteenth International Conference on Machine Learning, pages 179-186, 2002.
18. D. Haussler and P. M. Long. A generalization of Sauer's lemma. Journal of Combinatorial Theory, Series A, 71(2):219-240, 1995.
19. P. M. Long and L. Tan. PAC learning axis-aligned rectangles with respect to product distributions from multiple-instance examples. Machine Learning, 30(1):7-21, 1998. ISSN 0885-6125. (Pubitemid 128690572)
20. O.Maron and T. Lozano-Ṕerez. A framework for multiple-instance learning. In Advances in Neural Information Processing Systems 10, pages 570-576, 1998.
21. O. Maron and A. L. Ratan. Multiple-instance learning for natural scene classification. In Proceedings of the Fifteenth International Conference on Machine Learning, pages 341-349, 1998.
22. S.Mendelson. Rademacher averages and phase transitions in glivenko-cantelli classes. IEEE Transactions on Information Theory, 48(1):251-263, 2002. (Pubitemid 34125324)
23. S. Nash and A. Sofer. Linear and Nonlinear Programming. McGraw-Hill, New York, 1996.
24. L. Pitt and L. G. Valiant. Computational limitations on learning from examples. Technical report, Harvard University Aiken Computation Laboratory, July 1986.
25. D. Pollard. Convergence of Stochastic Processes. Springer-Verlag, 1984.
26. L. De Raedt. Attribute-value learning versus inductive logic programming: The missing links (extended abstract). In Proceedings of the 8th International Workshop on Inductive Logic Programming, pages 1-8, London, UK, 1998. Springer-Verlag. (Pubitemid 128110601)
27. G. Rätsch and M. K. Warmuth. Efficient margin maximizing with boosting. Journal of Machine Learning Research, 6:2131-2152, December 2005. (Pubitemid 41798123)
28. S. Sabato and N. Tishby. Homogeneous multi-instance learning with arbitrary dependence. In Proceedings of the Twenty Second Annual Conference on Computational Learning Theory, 2009.
29. S. Sabato, N. Srebro, and N. Tishby. Reducing label complexity by learning from bags. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, volume 9, pages 685-692, 2010.
30. N. Sauer. On the density of families of sets. Journal of Combinatorial Theory Series A, 13:145-147, 1972.
31. R. E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):1-40, 1999.
32. R.E. Schapire, Y. Freund, P. Bartlett, and W.S. Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5):1651-1686, October 1998. (Pubitemid 128376902)
33. S. Shalev-Shwartz, O. Shamir, and K. Learning kernel-based halfspaces with the zero-one loss. In Proceedings of the Twenty Third Annual Conference on Computational Learning Theory, 2010.
34. N. Srebro, K. Sridharan, and A. Tewari. Smoothness, low-noise and fast rates. CoRR, abs/1009.3896, 2010.
35. V. N. Vapnik and A. Ya. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, XVI(2):264-280, 1971.
36. J. von Neumann. Zur theorie der gesellschaftsspiele. Mathematische Annalen, 100:295-320, 1928.
37. N. Weidmann, E. Frank, and B. Pfahringer. A two-level learning method for generalized multiinstance problems. In Proceedings of the European conference on machine learning, pages 468-479, 2003. (Pubitemid 37231000)
38. Q. Zhang and S.A. Goldman. EM-DD: An improved multiple-instance learning technique. In Advances in Neural Information Processing Systems 14, 2001.
39. Z. Zhou, K. Jiang, and M. Li. Multi-instance learning based web mining. Applied Intelligence, 22 (2):135-147, 2005. (Pubitemid 40400730)