Multiple-instance learning with instance selection via dominant sets
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Instance selection for class imbalanced problems by means of selecting instances more than once
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Multiple instance learning for group record linkage
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Multiple-instance learning as a classifier combining problem
Pattern Recognition
In defence of negative mining for annotating weakly labelled data
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Salient instance selection for multiple-instance learning
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Robust multiple-instance learning with superbags
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
One-Class multiple instance learning and applications to target tracking
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Learning discriminative localization from weakly labeled data
Pattern Recognition
Multiple instance learning via Gaussian processes
Data Mining and Knowledge Discovery
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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 techniques to speed up the training process without compromising the performance. In this paper, we address the issue of instance selection in MIL. We propose MILIS, a novel MIL algorithm based on adaptive instance selection. We do this in an alternating optimization framework by intertwining the steps of instance selection and classifier learning in an iterative manner which is guaranteed to converge. Initial instance selection is achieved by a simple yet effective kernel density estimator on the negative instances. Experimental results demonstrate the utility and efficiency of the proposed approach as compared to the state of the art.