Network Security White Papers
Relaxed Online SVMs for Spam Filtering
Overview Spam is a key problem in electronic communication, including large-scale email systems and the growing number of blogs. Content-based filtering is one reliable method of combating this threat in its various forms, but some academic researchers and industrial practitioners disagree on how best to filter spam. The former have advocated the use of Support Vector Machines (SVMs) for content-based filtering, as this machine learning methodology gives state-of-the-art performance for text classification. This paper offers a resolution to this controversy. First, they show that online SVMs indeed give state-of-the-art classification performance on online spam filtering on large benchmark data sets. Second, they show that nearly equivalent performance may be achieved by a Relaxed Online SVM (ROSVM) at greatly reduced computational cost.
| Publisher | Association for Computing Machinery | File Format | |
|---|---|---|---|
| Date Published | July 2007 | ||
| Format | White Papers | ||
| Topics | |||
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