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Erschienen in:
2007 | OriginalPaper | Buchkapitel
An Improved Multiple-Instance Learning Algorithm
verfasst von : Fengqing Han, Dacheng Wang, Xiaofeng Liao
Erschienen in: Advances in Neural Networks – ISNN 2007
Verlag: Springer Berlin Heidelberg
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Multiple-instance learning (MIL) is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. In this paper a novel algorithm has been introduced for multiple-instance learning. This method was inspired by both diverse density (DD) and its expectation maximization version (EM-DD). It converts MIL problem to a single-instance setting. This improved method has better accuracy and time complexity than DD and EM-DD. We apply it to drug activity prediction and image retrieval. The experiments show it has competitive accuracy values compared with other previous approaches.