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GECCO
2008
Springer
137views Optimization» more  GECCO 2008»
15 years 7 months ago
Informative sampling for large unbalanced data sets
Selective sampling is a form of active learning which can reduce the cost of training by only drawing informative data points into the training set. This selected training set is ...
Zhenyu Lu, Anand I. Rughani, Bruce I. Tranmer, Jos...
KDD
2012
ACM
201views Data Mining» more  KDD 2012»
13 years 9 months ago
Learning from crowds in the presence of schools of thought
Crowdsourcing has recently become popular among machine learning researchers and social scientists as an effective way to collect large-scale experimental data from distributed w...
Yuandong Tian, Jun Zhu
CVPR
2010
IEEE
15 years 11 months ago
Visual Tracking via Weakly Supervised Learning from Multiple Imperfect Oracles
Long-term persistent tracking in ever-changing environments is a challenging task, which often requires addressing difficult object appearance update problems. To solve them, most...
Bineng Zhong, Hongxun Yao, Sheng Chen, Xiaotong Yu...
DIS
2004
Springer
15 years 10 months ago
Extracting Positive Attributions from Scientific Papers
The aim of our work is to provide support for reading (or skimming) scientific papers. In this paper we report on the task to identify concepts or terms with positive attributions ...
Son Bao Pham, Achim G. Hoffmann
MM
2010
ACM
127views Multimedia» more  MM 2010»
15 years 6 months ago
Mining and cropping common objects from images
Discovering common objects that appear frequently in a number of images is a challenging problem, due to (1) the appearance variations of the same common object and (2) the enormo...
Gangqiang Zhao, Junsong Yuan