An Instance Selection Approach to Multiple Instance Learning
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...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceeings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009)|
|01_Fu_An_Instance_Selection_Approach_2009.pdf||4.93 MB||Adobe PDF||Request a copy|
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