Confidence-weighted linear classification
Proceedings of the 25th international conference on Machine learning, 2008•dl.acm.org
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 …
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 …
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.