Multiple Instance Learning
(Redirected from Multiple -Instance Learning)
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See: Multiple Instance Learning Algorithm, Multiple Instance Learning Task, Multiple Instance Learning System.
References
2017
- (Ray et al., 2017) ⇒ Soumya Ray, Stephen Scott, and Hendrik Blockeel. (2017). “Multiple -Instance Learning”. In: (Sammut & Webb, 2017). DOI: 10.1007/978-1-4899-7687-1_578
- QUOTE: Multiple-instance (MI) learning is an extension of the standard supervised learning setting. In standard supervised learning, the input consists of a set of labeled instances each described by an attribute vector. The learner then induces a concept that relates the label of an instance to its attributes. In MI learning, the input consists of labeled examples (called “bags”) consisting of multisets of instances, each described by an attribute vector, and there are constraints that relate the label of each bag to the unknown labels of each instance. The MI learner then induces a concept that relates the label of a bag to the attributes describing the instances in it. This setting contains supervised learning as a special case: if each bag contains exactly one instance, it reduces to a standard supervised learning problem.