Confidence-Weighted Linear Classification for Text Categorization

نویسندگان

  • Koby Crammer
  • Mark Dredze
  • Fernando Pereira
چکیده

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 textcategorization 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.

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عنوان ژورنال:
  • Journal of Machine Learning Research

دوره 13  شماره 

صفحات  -

تاریخ انتشار 2012