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» A Comparison of Models for Cost-Sensitive Active Learning
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COLING
2010
11 years 9 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
IJCV
2011
264views more  IJCV 2011»
11 years 9 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
SDM
2010
SIAM
218views Data Mining» more  SDM 2010»
12 years 3 months ago
Confidence-Based Feature Acquisition to Minimize Training and Test Costs
We present Confidence-based Feature Acquisition (CFA), a novel supervised learning method for acquiring missing feature values when there is missing data at both training and test...
Marie desJardins, James MacGlashan, Kiri L. Wagsta...
EMNLP
2008
12 years 3 months ago
An Analysis of Active Learning Strategies for Sequence Labeling Tasks
Active learning is well-suited to many problems in natural language processing, where unlabeled data may be abundant but annotation is slow and expensive. This paper aims to shed ...
Burr Settles, Mark Craven
MMS
2006
12 years 1 months ago
Support vector machine active learning for music retrieval
Searching and organizing growing digital music collections requires a computational model of music similarity. This paper describes a system for performing flexible music similarit...
Michael I. Mandel, Graham E. Poliner, Daniel P. W....
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