Multiple-instance learning: Difference between revisions

Revision as of 20:50, 7 November 2012

Revision as of 09:03, 21 January 2013

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'''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 Amost 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}}.

Examples of where MIL is applied are:

* Molecule activity

* 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.

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| year = 1990 | pages = 557–563

| unused_data = Proceedings of the 1990 Conference on Advances in Neural Information Processing Systems (NIPS 3)}}.

*{{citation

| first1 = O. | last1 = Maron

| first2 = A.L. | last2 = Ratan

| title= Multiple-instance learning for natural scene classification

| year= 1998 | pages = 341-349

| unused_data Proceedings of the Fifteenth International Conference on Machine Learning}}.

[[Category:Machine learning]]