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» A Framework for Multiple-Instance Learning
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152
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GECCO
2010
Springer
191views Optimization» more  GECCO 2010»
15 years 5 months ago
Fitness importance for online evolution
To complement standard fitness functions, we propose "Fitness Importance" (FI) as a novel meta-heuristic for online learning systems. We define FI and show how it can be...
Philip Valencia, Raja Jurdak, Peter Lindsay
BMCBI
2008
100views more  BMCBI 2008»
15 years 5 months ago
High-precision high-coverage functional inference from integrated data sources
Background: Information obtained from diverse data sources can be combined in a principled manner using various machine learning methods to increase the reliability and range of k...
Bolan Linghu, Evan S. Snitkin, Dustin T. Holloway,...
TKDE
2008
116views more  TKDE 2008»
15 years 4 months ago
Long-Term Cross-Session Relevance Feedback Using Virtual Features
Relevance feedback (RF) is an iterative process, which refines the retrievals by utilizing the user's feedback on previously retrieved results. Traditional RF techniques solel...
Peng-Yeng Yin, Bir Bhanu, Kuang-Cheng Chang, Anlei...
ICDM
2010
IEEE
193views Data Mining» more  ICDM 2010»
15 years 2 months ago
Supervised Link Prediction Using Multiple Sources
Link prediction is a fundamental problem in social network analysis and modern-day commercial applications such as Facebook and Myspace. Most existing research approaches this pro...
Zhengdong Lu, Berkant Savas, Wei Tang, Inderjit S....
174
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CVPR
2011
IEEE
14 years 8 months ago
How does Person Identity Recognition Help Multi-Person Tracking?
We address the problem of multi-person tracking in a complex scene from a single camera. Although trackletassociation methods have shown impressive results in several challenging ...
Cheng-Hao Kuo, Ram Nevatia