Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 34, Issue 6, Pages 1068-1079Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2011.194
Keywords
Artificial intelligence; machine learning; nonsmooth optimization; bundle methods; multiple-instance learning; ranking; medicine and science
Funding
- Fonds quebecois de la recherche sur la nature et les technologies
- US Office of Naval Research [N00014-06-1-0014]
- US National Institutes of Health [R01LM009731]
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We present a bundle algorithm for multiple-instance classification and ranking. These frameworks yield improved models on many problems possessing special structure. Multiple-instance loss functions are typically nonsmooth and nonconvex, and current algorithms convert these to smooth nonconvex optimization problems that are solved iteratively. Inspired by the latest linear-time subgradient-based methods for support vector machines, we optimize the objective directly using a nonconvex bundle method. Computational results show this method is linearly scalable, while not sacrificing generalization accuracy, permitting modeling on new and larger data sets in computational chemistry and other applications. This new implementation facilitates modeling with kernels.
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