Synonyms
Definition
Multiple-Instance (MI) learning is an extension of the standard supervised learning setting. In standard supervised learning, the input consists of a set of labeled instances each described by an attribute vector. The learner then induces a concept that relates the label of an instance to its attributes. In MI learning, the input consists of labeled examples (called “bags”) consisting of multisets of instances, each described by an attribute vector, and there are constraints that relate the label of each bag to the unknown labels of each instance. The MI learner then induces a concept that relates the label of a bag to the attributes describing the instances in it. This setting contains supervised learning as a special case: if each bag contains exactly one instance, it reduces to a standard supervised learning problem.
Motivation and Background
The MI setting was introduced by Dietterich et al. (1997) in the context of drug activity...
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsRecommended Reading
Alphonse E, Matwin S (2002) Feature subset selection and inductive logic programming. In: Proceedings of the 19th international conference on machine learning, pp 11–18. Morgan Kaufmann, San Francisco
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 Press, Cambridge, MA, pp 561–568
Angluin D (1988) Queries and concept learning. Mach Learn 2(4):319–342
Auer P (1997) On learning from multi-instance examples: empirical evaluation of a theoretical approach. In: Proceeding of 14th international conference on machine learning, pp 21–29. Morgan Kaufmann, San Francisco
Auer P, Long PM, Srinivasan A (1998) Approximating hyper-rectangles: learning and pseudorandom sets. J Comput Syst Sci 57(3):376–388
Blockeel H, De Raedt L (1998) Top-down induction of first order logical decision trees. Artif Intell 101(1–2):285–297
Blockeel H, Page D, Srinivasan A (2005) Multi-instance tree learning. In: Proceedings of 22nd international conference on machine learning, Bonn, pp 57–64
Blum A, Kalai A (1998) A note on learning from multiple-instance examples. Mach Learn J 30(1):23–29
Cohen WW (1995) Fast effective rule induction. In: Proceedings of the 12th international conference on machine learning. Morgan Kaufmann, San Francisco
DeRaedt L (1998) Attribute-value learning versus inductive logic programming: the missing links. In: Proceedings of the eighth international conference on inductive logic programming. Springer, New York, pp 1–8
Dietterich T, Lathrop R, Lozano-Perez T (1997) Solving the multiple-instance problem with axis-parallel rectangles. Artif Intell 89(1–2):31–71
Dooly DR, Goldman SA, Kwek SS (2006) Real-valued multiple-instance learning with queries. J Comput Syst Sci 72(1):1–15
Dooly DR, Zhang Q, Goldman SA, Amar RA (2002) Multiple-instance learning of real-valued data. J Mach Learn Res 3:651–678
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, pp 179–186. Morgan Kaufmann, San Francisco
Goldman SA, Kwek SK, Scott SD (2001) Agnostic learning of geometric patterns. J Comput Syst Sci 6(1):123–151
Goldman SA, Scott SD (1999) A theoretical and empirical study of a noise-tolerant algorithm to learn geometric patterns. Mach Learn 37(1):5–49
Kearns M (1998) Efficient noise-tolerant learning from statistical queries. J ACM 45(6):983–1006
Long PM, Tan L (1998) PAC learning axis-aligned rectangles with respect to product distributions from multiple-instance examples. Mach Learn 30(1):7–21
Littlestone N (1988) Learning quickly when irrelevant attributes abound: a new linear-threshold algorithm. Mach Learn 2(4):285–318
Maron O (1998) Learning from ambiguity. PhD thesis, Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA
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, vol 10. MIT Press, Cambridge, MA, pp 570–576
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. Morgan Kaufmann, San Francisco, pp 361–368
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. AAAI Press, Menlo Park, pp 528–535
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 6:783–816
Papadimitriou C (1994) Computational complexity. Addison-Wesley, Boston
Pearl J (1998) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Mateo
Quinlan JR (1990) Learning logical definitions from relations. Mach Learn 5:239–266
Rahmani R, Goldman SA (2006) MISSL: Multiple-instance semi-supervised learning. In: Proceedings of the 23rd international conference on machine learning, pp 705–712. ACM Press, New York
Ramon J, DeRaedt L (2000) Multi instance neural networks. In: Proceedings of ICML-2000 workshop on attribute-value and relational learning
Ray S, Craven M (2005) Supervised versus multiple-instance learning: an empirical comparison. In: Proceedings of the 22nd international conference on machine learning. ACM Press, New York, pp 697–704
Ray S, Page D (2001) Multiple instance regression. In: Proceedings of the 18th international conference on machine learning. Morgan Kaufmann, Williamstown
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. Morgan Kaufmann, San Francisco, pp 779–806
Valiant LG (1984) A theory of the learnable. Commun ACM 27(11):1134–1142
Wang J, Zucker JD (2000) Solving the multiple-instance problem: a lazy learning approach. In: Proceedings of the 17th international conference on machine learning. Morgan Kaufmann, San Francisco, pp 1119–1125
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. Springer, Berlin/Heidelberg, pp 468–479
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 272–281
Zhang Q, Goldman S (2001) EM-DD: an improved multiple-instance learning technique. In: Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, pp 1073–1080
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. Morgan Kaufmann, San Francisco, pp 682–689
Zhou ZH, Zhang ML (2002) Neural networks for multi-instance learning. Technical Report, Nanjing University, Nanjing
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media New York
About this entry
Cite this entry
Ray, S., Scott, S., Blockeel, H. (2017). Multi-Instance Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_955
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7687-1_955
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4899-7685-7
Online ISBN: 978-1-4899-7687-1
eBook Packages: Computer ScienceReference Module Computer Science and Engineering