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• W2160648405 abstract "Confidence-weighted online learning is a generalization of margin-based learning of linear classifiers in which the margin constraint is replaced by a probabilistic constraint based on a distribution over classifier weights that is updated online as examples are observed. The distribution captures a notion of confidence on classifier weights, and in some cases it can also be interpreted as replacing a single learning rate by adaptive per-weight rates. Confidence-weighted learning was motivated by the statistical properties of natural-language classification tasks, where most of the informative features are relatively rare. We investigate several versions of confidence-weighted learning that use a Gaussian distribution over weight vectors, updated at each observed example to achieve high probability of correct classification for the example. Empirical evaluation on a range of text-categorization tasks show that our algorithms improve over other state-of-the-art online and batch methods, learn faster in the online setting, and lead to better classifier combination for a type of distributed training commonly used in cloud computing." @default.
• W2160648405 created "2016-06-24" @default.
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• W2160648405 date "2012-03-01" @default.
• W2160648405 modified "2023-09-26" @default.
• W2160648405 title "Confidence-weighted linear classification for text categorization" @default.
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