Spam Filtering using Support Vector Machine

Abstract

The traditional anti-spam techniques like Black and White List is not up to the mark in current scenario. The goal of Spam Classification is to distinguish between spam and legitimate mail message. But with the popularization of the Internet, it is challenging to develop spam filters that can effectively eliminate the increasing volumes of unwanted mails automatically before they enter a user's mailbox. Many researchers have been trying to separate spam from legitimate emails using machine learning algorithms based on statistical learning methods. In this paper, we evaluate the performance of Non Linear SVM based classifiers with various kernel functions over Enron Dataset.