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ICML
2008
IEEE

Confidence-weighted linear classification

15 years 25 days ago
Confidence-weighted linear classification
We introduce confidence-weighted linear classifiers, which add parameter confidence information to linear classifiers. Online learners in this setting update both classifier parameters and the estimate of their confidence. The particular online algorithms we study here maintain a Gaussian distribution over parameter vectors and update the mean and covariance of the distribution with each instance. Empirical evaluation on a range of NLP tasks show that our algorithm improves over other state of the art online and batch methods, learns faster in the online setting, and lends itself to better classifier combination after parallel training.
Mark Dredze, Koby Crammer, Fernando Pereira
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2008
Where ICML
Authors Mark Dredze, Koby Crammer, Fernando Pereira
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