|
#REDIRECT [[Multiple instance learning]] {{R from merge}}
{{orphan|date=December 2007}}
'''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}}.
Numerous researchers have worked on adapting classical classification techniques, such as [[support vector machines]] or [[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 = 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
| unused_data = |Proceedings of the 1990 Conference on Advances in Neural Information Processing Systems (NIPS 3)}}.
[[Category:Machine learning]]
| 