Searching for Relaxed Online Support Vector Machines For Spam Filtering information? Find all needed info by using official links provided below.
https://www.eecs.tufts.edu/~dsculley/papers/emailAndWebSpamSIGIR.pdf
spam, which is currently best combated by link analysis [13]. 1.1 An Anti-Spam Controversy The anti-spam community has been divided on the choice of the best machine learning method for content-based spam detection. Academic researchers have tended to favor the use of Support Vector Machines (SVMs), a statistically ro-
https://www.semanticscholar.org/paper/Relaxed-online-SVMs-for-spam-filtering-Sculley-Wachman/bd470476427bd459179d7d2c94e5b2c93ac0abd7
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 ...
https://www.eecs.tufts.edu/~dsculley/papers/trec.2007.spam.pdf
Relaxed Online Support Vector Machines (ROSVMs) have recently been proposed as an efficient methodology for attaining an approximate SVM solution for streaming data such as the online spam filtering task. Here, we apply ROSVMs in the TREC 2007 Spam filtering track and report results. In particular, we explore the effect of various sliding-
http://axon.cs.byu.edu/Dan/778/papers/Active%20Learning/sculley1.pdf
spam, which is currently best combated by link analysis [13]. 1.1 An Anti-Spam Controversy The anti-spam community has been divided on the choice of thebest machine learning method for content-based spam detection. Academic researchers have tended to favor the use of Support Vector Machines (SVMs), a statistically ro-
https://dl.acm.org/doi/10.1145/1277741.1277813
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. However, similar performance gains have yet to be demonstrated for online spam filtering.
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000005681610
Spam filtering is a classical online learning problem. When the size of training sample set becomes larger and larger, the speed of Online SVM is becoming slower and slower. Therefore, we relax the constraints of Online SVM and get the Relaxed Online SVM (ROSVM) model, which can not only improve the speed, but also can ensure the performance.
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