Multiple-instance learning
- Multiple-instance learning
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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 Dietterich, Lathrop & Lozano-Pérez (1997), but earlier examples of similar research exist, for instance in the work on handwritten digit recognition by 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
- Dietterich, Thomas G.; Lathrop, Richard H.; Lozano-Pérez, Tomás (1997), "Solving the multiple instance problem with axis-parallel rectangles", Artificial Intelligence 89 (1–2): 31–71, doi:10.1016/S0004-3702(96)00034-3 .
- Keeler, James D.; Rumelhart, David E.; Leow, Wee-Kheng (1990), Integrated segmentation and recognition of hand-printed numerals, pp. 557–563 .
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