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2009 IEEE Conference on Computer Vision and Pattern Recognition (2009)
Miami, FL, USA
June 20, 2009 to June 25, 2009
ISBN: 978-1-4244-3992-8
pp: 911-918
Zhouyu Fu , Gippsland Sch. of IT, Monash Univ., Churchill, VIC, Australia
ABSTRACT
Multiple-instance learning (MIL) is a new paradigm of supervised learning that deals with the classification of bags. Each bag is presented as a collection of instances from which features are extracted. In MIL, we have usually confronted with a large instance space for even moderately sized data sets since each bag may contain many instances. Hence it is important to design efficient instance pruning and selection techniques to speed up the learning process without compromising on the performance. In this paper, we address the issue of instance selection in multiple instance learning and propose the IS-MIL, an instance selection framework for MIL, to tackle large-scale MIL problems. IS-MIL is based on an alternative optimisation framework by iteratively repeating the steps of instance selection/updating and classifier learning, which is guaranteed to converge. Experimental results demonstrate the utility and efficiency of the proposed approach compared to the alternatives.
INDEX TERMS
iterative repetition, instance selection, multiple instance learning, supervised learning, bag classification, feature extraction, instance pruning, classifier learning
CITATION

A. Robles-Kelly and Zhouyu Fu, "An instance selection approach to Multiple instance Learning," 2009 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Miami, FL, USA, 2009, pp. 911-918.
doi:10.1109/CVPRW.2009.5206655
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