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Content Provider | IEEE Xplore Digital Library |
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Author | Weixin Li Vasconcelos, N. |
Copyright Year | 2015 |
Description | Author affiliation: Univ. of California, San Diego, La Jolla, CA, USA (Weixin Li; Vasconcelos, N.) |
Abstract | A generalized formulation of the multiple instance learning problem is considered. Under this formulation, both positive and negative bags are soft, in the sense that negative bags can also contain positive instances. This reflects a problem setting commonly found in practical applications, where labeling noise appears on both positive and negative training samples. A novel bag-level representation is introduced, using instances that are most likely to be positive (denoted top instances), and its ability to separate soft bags, depending on their relative composition in terms of positive and negative instances, is studied. This study inspires a new large-margin algorithm for soft-bag classification, based on a latent support vector machine that efficiently explores the combinatorial space of bag compositions. Empirical evaluation on three datasets is shown to confirm the main findings of the theoretical analysis and the effectiveness of the proposed soft-bag classifier. |
Starting Page | 4277 |
Ending Page | 4285 |
File Size | 412698 |
Page Count | 9 |
File Format | |
ISSN | 10636919 |
e-ISBN | 9781467369640 |
DOI | 10.1109/CVPR.2015.7299056 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2015-06-07 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Supervised learning Support vector machines Labeling Noise Particle separators Kernel Algorithm design and analysis |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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