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CEAS
2007
Springer
11 years 10 months ago
Online Active Learning Methods for Fast Label-Efficient Spam Filtering
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 trustwo...
D. Sculley
AIRS
2008
Springer
12 years 1 months ago
Active Learning for Online Spam Filtering
Spam filtering is defined as a task trying to label emails with spam or ham in an online situation. The online feature requires the spam filter has a strong timely generalization a...
Wuying Liu, Ting Wang
JMLR
2006
125views more  JMLR 2006»
11 years 6 months ago
Spam Filtering Using Statistical Data Compression Models
Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade fi...
Andrej Bratko, Gordon V. Cormack, Bogdan Filipic, ...
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
12 years 7 months ago
Practical learning from one-sided feedback
In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class. Such applications include spam filt...
D. Sculley
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