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EMNLP
2004
15 years 4 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
EUC
2006
Springer
15 years 6 months ago
On Multiprocessor Utility Accrual Real-Time Scheduling with Statistical Timing Assurances
We present the first Utility Accrual (or UA) real-time scheduling algorithm for multiprocessors, called gMUA. The algorithm considers an application model where real-time activiti...
Hyeonjoong Cho, Haisang Wu, Binoy Ravindran, E. Do...
UAI
2004
15 years 4 months ago
Active Model Selection
Classical learning assumes the learner is given a labeled data sample, from which it learns a model. The field of Active Learning deals with the situation where the learner begins...
Omid Madani, Daniel J. Lizotte, Russell Greiner
IJCV
2011
264views more  IJCV 2011»
14 years 10 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
CORR
2010
Springer
171views Education» more  CORR 2010»
14 years 9 months ago
Online Learning in Opportunistic Spectrum Access: A Restless Bandit Approach
We consider an opportunistic spectrum access (OSA) problem where the time-varying condition of each channel (e.g., as a result of random fading or certain primary users' activ...
Cem Tekin, Mingyan Liu