akehurst2004.pdf (159.79 kB)
Comparing content-filter techniques for stopping spam
conference contribution
posted on 2008-11-26, 08:44 authored by Andrew Akehurst, Iain PhillipsIain Phillips, Mark S. WithallThere are many new theoretical techniques
for detecting spam e-mail based
upon the message contents. Although
Bayesian methods are the most wellknown,
there are other approaches for
classifying information. This paper establishes
some criteria for measuring
spam filter effectiveness and compares the
Boosting and Support Vector Machine
approaches with some well-known existing
filter software. It also examines ways
of transforming e-mail messages into a
form which is more readily processable by
such algorithms.
History
School
- Science
Department
- Computer Science
Citation
AKEHURST, A., PHILLIPS, I. and WITHALL, M., 2004. Comparing content-filter techniques for stopping spam. Workshop on Computational Intelligence (UKCI 2004), 6-8 September, Loughborough UniversityVersion
- NA (Not Applicable or Unknown)
Publication date
2004Notes
This is a conference paper.Language
- en