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» Semi-Supervised Active Learning for Sequence Labeling
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EMNLP
2009
13 years 5 months ago
Active Learning by Labeling Features
Methods that learn from prior information about input features such as generalized expectation (GE) have been used to train accurate models with very little effort. In this paper,...
Gregory Druck, Burr Settles, Andrew McCallum
ECCV
2008
Springer
14 years 9 months ago
Learning to Recognize Activities from the Wrong View Point
Appearance features are good at discriminating activities in a fixed view, but behave poorly when aspect is changed. We describe a method to build features that are highly stable u...
Ali Farhadi, Mostafa Kamali Tabrizi
ALT
2009
Springer
14 years 4 months ago
Average-Case Active Learning with Costs
Abstract. We analyze the expected cost of a greedy active learning algorithm. Our analysis extends previous work to a more general setting in which different queries have differe...
Andrew Guillory, Jeff A. Bilmes
ICASSP
2011
IEEE
12 years 11 months ago
Exploiting active-learning strategies for annotating prosodic events with limited labeled data
Many applications of spoken-language systems can benefit from having access to annotations of prosodic events. Unfortunately, obtaining human annotations of these events, even se...
Raul Fernandez, Bhuvana Ramabhadran
CVPR
2008
IEEE
14 years 1 months ago
3D face tracking and expression inference from a 2D sequence using manifold learning
We propose a person-dependent, manifold-based approach for modeling and tracking rigid and nonrigid 3D facial deformations from a monocular video sequence. The rigid and nonrigid ...
Wei-Kai Liao, Gérard G. Medioni