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KDD
2010
ACM
247views Data Mining» more  KDD 2010»
13 years 10 months ago
Active learning for biomedical citation screening
Active learning (AL) is an increasingly popular strategy for mitigating the amount of labeled data required to train classifiers, thereby reducing annotator effort. We describe ...
Byron C. Wallace, Kevin Small, Carla E. Brodley, T...
ICML
2003
IEEE
14 years 9 months ago
Linear Programming Boosting for Uneven Datasets
The paper extends the notion of linear programming boosting to handle uneven datasets. Extensive experiments with text classification problem compare the performance of a number o...
Jure Leskovec, John Shawe-Taylor
NIPS
2007
13 years 10 months ago
Multiple-Instance Active Learning
We present a framework for active learning in the multiple-instance (MI) setting. In an MI learning problem, instances are naturally organized into bags and it is the bags, instea...
Burr Settles, Mark Craven, Soumya Ray
SIGMOD
2006
ACM
131views Database» more  SIGMOD 2006»
14 years 8 months ago
An automatic construction and organization strategy for ensemble learning on data streams
As data streams are gaining prominence in a growing number of emerging application domains, classification on data streams is becoming an active research area. Currently, the typi...
Yi Zhang, Xiaoming Jin
ICML
2009
IEEE
14 years 9 months ago
Uncertainty sampling and transductive experimental design for active dual supervision
Dual supervision refers to the general setting of learning from both labeled examples as well as labeled features. Labeled features are naturally available in tasks such as text c...
Vikas Sindhwani, Prem Melville, Richard D. Lawrenc...