Multiple-instance learning: Difference between revisions

Revision as of 10:30, 17 April 2009

Revision as of 20:13, 27 April 2010

Line 1:

{{orphan|date=December 2007}}

'''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''' is a variation on [[supervised learning]], where the task is to learn a concept from data consisting of a sequence of instances, each labeled as positive or negative, and each described as a [[set]] of [[Coordinate vector|vectors]]. The instance is positive if at least one of the vectors in its set lies within the intended concept, and negative if none of the vectors lies within the concept; the task is to learn an accurate description of the concept from this information.

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]] [[digit]] [[optical character recognition|recognition]] by {{harvtxt|Keeler|Rumelhart|Leow|1990}}.