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MILIS: Multiple Instance Learning with Instance Selection

Fu, Zhouyu; Robles-Kelly, Antonio; Zhou, Jun


Multiple instance learning (MIL) is a paradigm in supervised learning that deals with the classification of collections of instances called bags. Each bag contains a number of instances from which features are extracted. The complexity of MIL is largely dependent on the number of instances in the training data set. Since we are usually confronted with a large instance space even for moderately sized real-world data sets applications, it is important to design efficient instance selection...[Show more]

CollectionsANU Research Publications
Date published: 2011
Type: Journal article
Source: IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI: 10.1109/TPAMI.2010.155


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