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#REDIRECT [[Multiple instance learning]] {{R from merge}}
'''Multiple-instance learning''' (MIL) is a variation on [[supervised learning]]. Instead of receiving a set of instances which are labeled positive or negative, the learner receives a set of ''bags'' that are labeled positive or negative. Each bag contains many instances. The most common assumption is that a bag is labeled negative if all the instances in it are negative. On the other hand, a bag is labeled positive if there is at least one instance in it which is positive. From a collection of labeled bags, the learner tries to either (i) induce a concept that will label individual instances correctly or (ii) learn how to label bags without inducing the concept.
Multiple-instance learning was originally proposed under this name by {{harvtxt|Dietterich|Lathrop|Lozano-Pérez|1997}}, but earlier examples of similar research exist, for instance in the work on [[handwriting|handwritten]] [[Numerical digit|digit]] [[optical character recognition|recognition]] by {{harvtxt|Keeler|Rumelhart|Leow|1990}}. Recent reviews of the MIL literature include {{harvtxt|Amores|2013}}, which provides an extensive review and comparative study of the different paradigms, and {{harvtxt|Foulds|Frank|2010}}, which provides a thorough review of the different assumptions used by different paradigms in the literature.
Examples of where MIL is applied are:
* Molecule activity
* Predicting function for alternatively spliced isoforms {{harvtxt|Li|Menon|et al.|2014}},{{harvtxt|Eksi|Li|Menon|et al.|2013}}
* Image classification {{harvtxt|Maron|Ratan|1998}}
* Text or document categorization
Numerous researchers have worked on adapting classical classification techniques, such as [[support vector machines]] or [[Boosting (meta-algorithm)|boosting]], to work within the context of multiple-instance learning.
==References==
*{{citation
| first1 = Thomas G. | last1 = Dietterich
| first2 = Richard H. | last2 = Lathrop
| first3 = Tomás | last3 = Lozano-Pérez
| title = Solving the multiple instance problem with axis-parallel rectangles
| journal = Artificial Intelligence
| volume = 89 | issue = 1–2 | year = 1997 | pages = 31–71 | doi = 10.1016/S0004-3702(96)00034-3}}.
*{{citation
| first1 = Jaume| last1 = Amores
| title = Multiple instance classification: Review, taxonomy and comparative study
| journal = Artificial Intelligence
| volume = 201 | year = 2013 | pages = 81–105 | doi = 10.1016/j.artint.2013.06.003}}.
*{{citation
| first1 = James | last1 = Foulds
| first2 = Eibe | last2 = Frank
| title = A Review of Multi-Instance Learning Assumptions
| journal = Knowledge Engineering Review
| volume = 25 | issue = 1 | year = 2010 | pages = 1–25 | doi = 10.1017/S026988890999035X}}.
*{{citation
| first1 = James D. | last1 = Keeler
| first2 = David E. | last2 = Rumelhart
| first3 = Wee-Kheng | last3 = Leow
| title = Integrated segmentation and recognition of hand-printed numerals
| year = 1990 | pages = 557–563
| work = Proceedings of the 1990 Conference on Advances in Neural Information Processing Systems (NIPS 3)}}.
*{{citation
| first1 = H.D. | last1 = Li
| first2 = R. | last2 = Menon
| first3 = | last3 = et al.
| title= The emerging era of genomic data integration for analyzing splice isoform function
| year= 2014 | pii = S0168-9525(14)00085-7
| journal = Trends in Genetics
| doi = 10.1016/j.tig.2014.05.005}}.
*{{citation
| first1 = R. | last1 = Eksi
| first2 = H.D. | last2 = Li
| first3 = R. | last3 = Menon
| first4 = | last4 = et al.
| title= Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data
| year= 2013 | pages = Nov;9(11):e1003314
| journal = PLoS Comput Biol
| doi = 10.1371/journal.pcbi.1003314}}.
*{{citation
| first1 = O. | last1 = Maron
| first2 = A.L. | last2 = Ratan
| title= Multiple-instance learning for natural scene classification
| year= 1998 | pages = 341-349
| work= Proceedings of the Fifteenth International Conference on Machine Learning}}.
[[Category:Machine learning]]
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