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Spam E-Mail Characterization: An Experimental Performance Comparison Of Machine Learning
2017
Zenodo
The increasing volume of unsolicited mass e-mail (otherwise called spam) has generated a need for reliable against spam filters. Utilizing a classifier based on machine learning techniques to naturally filter out spam e-mail has drawn many researchers' attention. In this paper, we review some of relevant ideas and do a set of systematic experiments on e-mail categorization, which has been conducted with four machine learning calculations applied to different parts of e-mail. Experimental
doi:10.5281/zenodo.1016645
fatcat:c46mmbrjq5eenmz3665htt2ply