Multiple-instance learning: Difference between revisions

Revision as of 07:40, 28 December 2016

Revision as of 07:23, 29 December 2016

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{{mergetomerge to|Multiple_instance_learning|discuss=Talk:Multiple_instance_learning#Merger proposal|date=September 2016}}

In [[machine learning]], '''multiple-instance learning''' (MIL) is a variation on [[supervised learning]]. Instead of receiving a set of instances which are individually labeled, the learner receives a set of labeled ''bags'', each containing many instances. In the simple case of multiple-instance [[binary classification]], a bag may be 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.

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| pmid=25614300

| doi=10.1038/srep08004 | pages=8004}}

*Zhu, Wentao; Lou, Qi; Vang, Yeeleng Scott; Xie, Xiaohui (2016), "[https://arxiv.org/abs/1612.05968 Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification]",  ''arXiv preprint arXiv:1612.05968''.

[[Category:Machine learning]]