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Multiple-Instance Learning Via Random Walk.
- Source :
- Machine Learning: ECML 2006; 2006, p473-484, 12p
- Publication Year :
- 2006
-
Abstract
- This paper presents a decoupled two stage solution to the multiple-instance learning (MIL) problem. With a constructed affinity matrix to reflect the instance relations, a modified Random Walk on a Graph process is applied to infer the positive instances in each positive bag. This process has both a closed form solution and an efficient iterative one. Combined with the Support Vector Machine (SVM) classifier, this algorithm decouples the inferring and training stages and converts MIL into a supervised learning problem. Compared with previous algorithms on several benchmark data sets, the proposed algorithm is quite competitive in both computational efficiency and classification accuracy. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540453758
- Database :
- Complementary Index
- Journal :
- Machine Learning: ECML 2006
- Publication Type :
- Book
- Accession number :
- 32905635
- Full Text :
- https://doi.org/10.1007/11871842_45