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 …

The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a
need for reliable anti-spam filters. Machine learning techniques now days used to …

The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a
need for reliable antispam filters. Using a classifier based on machine learning techniques …

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 …

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 …

The growth of email users has resulted in the dramatic increasing of the spam emails.
Helpfully, there are different approaches able to automatically detect and remove most of …

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 …

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 …

Spam filtering is a text categorization task that shows especial features that make it
interesting and difficult. First, the task has been performed traditionally using heuristics from …

This paper describes a set of comparative experiments for the problem of automatically
filtering unwanted electronic mail messages. Several variants of the AdaBoost algorithm with …