In this paper, we present a comprehensive review of recent developments in the application
of machine learning algorithms to Spam filtering, focusing on both textual-and image-based …

This paper evaluates five supervised learning methods in the context of statistical spam
filtering. We study the impact of different feature pruning methods and feature set sizes on …

The Internet emerged as a powerful infrastructure for the worldwide communication and
interaction of people. Some unethical uses of this technology (for instance spam or viruses) …

This paper provides an overview of current and potential future spam filtering approaches.
We examine the problems spam introduces, what spam is and how we can measure it. The …

It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited
bulk e-mail (" spam"). We conduct a thorough evaluation of this proposal on a corpus that we …

The article gives an overview of some of the most popular machine learning methods
(Bayesian classification, k-NN, ANNs, SVMs) and of their applicability to the problem of …

This paper explores the use of the naive Bayes classifier as the basis for personalised spam
filters. Several machine learning algorithms, including variants of naive Bayes, have …

Email spam is one of the major problems of the today's Internet, bringing financial damage to
companies and annoying individual users. Among the approaches developed to stop spam …

E-mail spam has become an increasingly important problem with a big economic impact in
society. Fortunately, there are different approaches allowing to automatically detect and …

We investigate the performance of two machine learning algorithms in the context of anti-
spam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a …