Online Active Learning Methods for Fast Label-Efficient Spam Filtering

@inproceedings{Sculley2007OnlineAL,
  title={Online Active Learning Methods for Fast Label-Efficient Spam Filtering},
  author={D. Sculley},
  booktitle={CEAS},
  year={2007}
}
Active learning methods seek to reduce the number of labeled examples needed to train an effective classifier, and have natural appeal in spam filtering applications where trustworthy labels for messages may be costly to acquire. Past investigations of active learning in spam filtering have focused on the pool-based scenario, where there is assumed to be a large, unlabeled data set and the goal is to iteratively identify the best subset of examples for which to request labels. However, even… CONTINUE READING

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