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EMNLP
2004
13 years 6 months ago
Active Learning and the Total Cost of Annotation
Active learning (AL) promises to reduce the cost of annotating labeled datasets for trainable human language technologies. Contrary to expectations, when creating labeled training...
Jason Baldridge, Miles Osborne
IJCV
2011
264views more  IJCV 2011»
12 years 11 months ago
Cost-Sensitive Active Visual Category Learning
Abstract We present an active learning framework that predicts the tradeoff between the effort and information gain associated with a candidate image annotation, thereby ranking un...
Sudheendra Vijayanarasimhan, Kristen Grauman
NIPS
2008
13 years 6 months ago
Multi-Level Active Prediction of Useful Image Annotations for Recognition
We introduce a framework for actively learning visual categories from a mixture of weakly and strongly labeled image examples. We propose to allow the categorylearner to strategic...
Sudheendra Vijayanarasimhan, Kristen Grauman
COLING
2010
12 years 11 months ago
A Comparison of Models for Cost-Sensitive Active Learning
Active Learning (AL) is a selective sampling strategy which has been shown to be particularly cost-efficient by drastically reducing the amount of training data to be manually ann...
Katrin Tomanek, Udo Hahn
ACL
2008
13 years 6 months ago
Assessing the Costs of Sampling Methods in Active Learning for Annotation
Traditional Active Learning (AL) techniques assume that the annotation of each datum costs the same. This is not the case when annotating sequences; some sequences will take longe...
Robbie Haertel, Eric K. Ringger, Kevin D. Seppi, J...