Multiple-instance learning: Difference between revisions

Revision as of 14:09, 23 August 2012

Revision as of 20:50, 7 November 2012

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'''Multiple-instance learning''' 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. 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 induce a concept that will label individual instances correctly.

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}}.

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.