Encyclopedia of Machine Learning and Data Mining

Living Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Multiple-Instance Learning

  • Soumya Ray
  • Stephen  Scott
  • Hendrik  Blockeel
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7502-7_578-1

Keywords

Inductive Logic Programming Negative Instance Disjunctive Normal Form Supervise Learning Algorithm Maximal Common Subgraph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Recommended Reading

  1. Alphonse E, Matwin S (2002) Feature subset selection and inductive logic programming. In: Proceedings of the 19th international conference on machine learning, Sydney. Morgan Kaufmann, San Francisco, pp 11–18Google Scholar
  2. Andrews S, Tsochantaridis I, Hofmann T (2003) Support vector machines for multiple-instance learning. In: Becker S, Thrun S, Obermayer K (eds) Advances in neural information processing systems, vol 15. MIT, Cambridge, pp 561–568Google Scholar
  3. Angluin D (1988) Queries and concept learning. Mach Learn 2(4):319–342Google Scholar
  4. Auer P (1997) On learning from multi-instance examples: empirical evaluation of a theoretical approach. In: Proceedings of the 14th international conference on machine learning, Nashville. Morgan Kaufmann, San Francisco, pp 21–29Google Scholar
  5. Auer P, Long PM, Srinivasan A (1998) Approximating hyper-rectangles: learning and pseudorandom sets. J Comput Syst Sci 57(3):376–388MATHMathSciNetCrossRefGoogle Scholar
  6. Blockeel H, De Raedt L (1998) Top-down induction of first order logical decision trees. Artif Intell 101(1–2):285–297MATHCrossRefGoogle Scholar
  7. Blockeel H, Page D, Srinivasan A (2005) Multi-instance tree learning. In: Proceedings of 22nd international conference on machine learning, Bonn, pp 57–64Google Scholar
  8. Blum A, Kalai A (1998) A note on learning from multiple-instance examples. Mach Learn J 30(1):23–29MATHCrossRefGoogle Scholar
  9. Cohen WW (1995) Fast effective rule induction. In: Proceedings of the 12th international conference on machine learning, Tahoe City. Morgan Kaufmann, San FranciscoGoogle Scholar
  10. DeRaedt L (1998) Attribute-value learning versus inductive logic programming: the missing links. In: Proceedings of the eighth international conference on inductive logic programming, Madison. Springer, New York, pp 1–8CrossRefGoogle Scholar
  11. Dietterich T, Lathrop R, Lozano-Perez T (1997) Solving the multiple-instance problem with axis-parallel rectangles. Artif Intell 89(1–2):31–71MATHCrossRefGoogle Scholar
  12. Dooly DR, Goldman SA, Kwek SS (2006) Real-valued multiple-instance learning with queries. J Comput Syst Sci 72(1):1–15MATHMathSciNetCrossRefGoogle Scholar
  13. Dooly DR, Zhang Q, Goldman SA, Amar RA (2002) Multiple-instance learning of real-valued data. J Mach Learn Res 3:651–678Google Scholar
  14. Gartner T, Flach PA, Kowalczyk A, Smola AJ (2002) Multi-instance kernels. In: Sammut C, Hoffmann A (eds) Proceedings of the 19th international conference on machine learning, Sydney. Morgan Kaufmann, San Francisco, pp 179–186Google Scholar
  15. Goldman SA, Kwek SK, Scott SD (2001) Agnostic learning of geometric patterns. J Comput Syst Sci 6(1):123–151MathSciNetCrossRefGoogle Scholar
  16. Goldman SA, Scott SD (1999) A theoretical and empirical study of a noise-tolerant algorithm to learn geometric patterns. Mach Learn 37(1):5–49MATHCrossRefGoogle Scholar
  17. Kearns M (1998) Efficient noise-tolerant learning from statistical queries. J ACM 45(6):983–1006MATHMathSciNetCrossRefGoogle Scholar
  18. Long PM, Tan L (1998) PAC learning axis-aligned rectangles with respect to product distributions from multiple-instance examples. Mach Learn 30(1):7–21MATHCrossRefGoogle Scholar
  19. Littlestone N (1988) Learning quickly when irrelevant attributes abound: a new linear-threshold algorithm. Mach Learn 2(4):285–318Google Scholar
  20. Maron O (1998) Learning from ambiguity. PhD thesis, Department of Electrical Engineering and Computer Science, MIT, CambridgeGoogle Scholar
  21. Maron O, Lozano-Pérez T (1998) A framework for multiple-instance learning. In: Jordan MI, Kearns MJ, Solla SA (eds) Advances in neural information processing systems, Denver, vol 10. MIT, Cambridge, pp 570–576Google Scholar
  22. McGovern A, Barto AG (2001) Automatic discovery of subgoals in reinforcement learning using diverse density. In: Proceedings of the 18th international conference on machine learning, Williamstown. Morgan Kaufmann, San Francisco, pp 361–368Google Scholar
  23. McGovern A, Jensen D (2003) Identifying predictive structures in relational data using multiple instance learning. In: Proceedings of the 20th international conference on machine learning, Washington, DC. AAAI, Menlo Park, pp 528–535Google Scholar
  24. Murray JF, Hughes GF, Kreutz-Delgado K (2005) Machine learning methods for predicting failures in hard drives: a multiple-instance application. J Mach Learn Res 6:783–816MathSciNetGoogle Scholar
  25. Papadimitriou C (1994) Computational complexity. Addison-Wesley, BostonMATHGoogle Scholar
  26. Pearl J (1998) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San MateoGoogle Scholar
  27. Quinlan JR (1990) Learning logical definitions from relations. Mach Learn 5:239–266Google Scholar
  28. Rahmani R, Goldman SA (2006) MISSL: multiple-instance semi-supervised learning. In: Proceedings of the 23rd international conference on machine learning, Pittsburgh. ACM, New York, pp 705–712Google Scholar
  29. Ramon J, DeRaedt L (2000) Multi instance neural networks. In: Proceedings of ICML-2000 workshop on attribute-value and relational learningGoogle Scholar
  30. Ray S, Craven M (2005) Supervised versus multiple-instance learning: an empirical comparison. In: Proceedings of the 22nd international conference on machine learning, Bonn. ACM, New York, pp 697–704Google Scholar
  31. Ray S, Page D (2001) Multiple instance regression. In: Proceedings of the 18th international conference on machine learning, Williamstown. Morgan KaufmannGoogle Scholar
  32. Tao Q, Scott SD, Vinodchandran NV (2004) SVM-based generalized multiple-instance learning via approximate box counting. In: Proceedings of the 21st international conference on machine learning, Banff. Morgan Kaufmann, San Francisco, pp 779–806Google Scholar
  33. Valiant LG (1984) A theory of the learnable. Commun ACM 27(11):1134–1142MATHCrossRefGoogle Scholar
  34. Wang J, Zucker JD (2000) Solving the multiple-instance problem: a lazy learning approach. In: Proceedings of the 17th international conference on machine learning, Stanford. Morgan Kaufmann, San Francisco, pp 1119–1125Google Scholar
  35. Weidmann N, Frank E, Pfahringer B (2003) A two-level learning method for generalized multi-instance problems. In: Proceedings of the European conference on machine learning, Cavtat-Dubrovnik. Springer, Berlin/Heidelberg, pp 468–479Google Scholar
  36. Xu X, Frank E (2004) Logistic regression and boosting for labeled bags of instances. In: Proceedings of the Pacific-Asia conference on knowledge discovery and data mining, Sydney, pp 272–281MATHGoogle Scholar
  37. Zhang Q, Goldman S (2001) EM-DD: an improved multiple-instance learning technique. In: Advances in neural information processing systems, Vancouver. MIT, pp 1073–1080Google Scholar
  38. Zhang Q, Yu W, Goldman S, Fritts J (2002) Content-based image retrieval using multiple-instance learning. In: Proceedings of the 19th international conference on machine learning, Sydney. Morgan Kaufmann, San Francisco, pp 682–689Google Scholar
  39. Zhou ZH, Zhang ML (2002) Neural networks for multi-instance learning. Technical report, Nanjing University, NanjingGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Case Western Reserve UniversityOHUSA
  2. 2.University of NebraskaNEUSA
  3. 3.K. U. LeuvenHeverleeBelgium