We introduce confidence-weighted linear classifiers, a new class of algorithms that maintain confidence information about classifier parameters. Learning in this framework updates parameters by estimating weights and increasing model confidence. We investigate a new online algorithm that maintains a Gaussian distribution over weight vectors, updating the mean and variance of the model 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.
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