Synonyms
The relationship between supervised, multiple-instance (MI), and relational learning. (a) In supervised learning, each example (geometric figure) is labeled. A possible concept that explains the example labels shown is “the figure is a rectangle.” (b) In MI learning, bags of examples are labeled. A possible concept that explains the bag labels shown is “the bag contains at least one figure that is a rectangle.” (c) In relational learning, objects of arbitrary structure are labeled. A possible concept that explains the object labels shown is “the object is a stack of three figures and the bottom figure is a rectangle”
This is a preview of subscription content, log in via an institution to check access.
Recommended Reading
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 11–18
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 561–568
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: Proceedings of the 14th international conference on machine learning, Nashville. Morgan Kaufmann, San Francisco, pp 21–29
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, Tahoe City. 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, Madison. 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, Sydney. Morgan Kaufmann, San Francisco, pp 179–186
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
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 570–576
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, Williamstown. 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, Washington, DC. AAAI, 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, Pittsburgh. ACM, New York, pp 705–712
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, Bonn. ACM, New York, pp 697–704
Ray S, Page D (2001) Multiple instance regression. In: Proceedings of the 18th international conference on machine learning, Williamstown. Morgan Kaufmann
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 779–806
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, Stanford. 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, Cavtat-Dubrovnik. 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, Vancouver. MIT, 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, Sydney. 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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this entry
Cite this entry
Ray, S., Scott, S., Blockeel, H. (2014). Multiple-Instance Learning. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_578-1
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7502-7_578-1
Received:
Accepted:
Published:
Publisher Name: Springer, Boston, MA
Online ISBN: 978-1-4899-7502-7
eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering