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An Improved Multiple-Instance Learning Algorithm.
- Source :
- Advances in Neural Networks: ISNN 2007 (9783540723820); 2007, p1104-1109, 6p
- Publication Year :
- 2007
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540723820
- Database :
- Complementary Index
- Journal :
- Advances in Neural Networks: ISNN 2007 (9783540723820)
- Publication Type :
- Book
- Accession number :
- 33176520
- Full Text :
- https://doi.org/10.1007/978-3-540-72383-7_129