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- Multiple-instance_learning abstract "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 most 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 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). Recent reviews of the MIL literature include Amores (2013), which provides an extensive review and comparative study of the different paradigms, and Foulds & Frank (2010), which provides a thorough review of the different assumptions used by different paradigms in the literature.Examples of where MIL is applied are: Molecule activity Predicting function for alternatively spliced isoforms Eksi et al. (2013) Image classification Maron & Ratan (1998) Text or document categorizationNumerous researchers have worked on adapting classical classification techniques, such as support vector machines or boosting, to work within the context of multiple-instance learning.".
- Multiple-instance_learning wikiPageID "14082194".
- Multiple-instance_learning wikiPageRevisionID "599845844".
- Multiple-instance_learning hasPhotoCollection Multiple-instance_learning.
- Multiple-instance_learning subject Category:Machine_learning.
- Multiple-instance_learning comment "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 most 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.".
- Multiple-instance_learning label "Multiple-instance learning".
- Multiple-instance_learning sameAs m.03cszdd.
- Multiple-instance_learning sameAs Q6934870.
- Multiple-instance_learning sameAs Q6934870.
- Multiple-instance_learning wasDerivedFrom Multiple-instance_learning?oldid=599845844.
- Multiple-instance_learning isPrimaryTopicOf Multiple-instance_learning.