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Ending Spam: Bayesian Content Filtering & the Art of Statistical Language Classification
Join author John Zdziarski for a look inside the brilliant
minds that have conceived clever new ways to fight spam in
all its nefarious forms. This landmark title describes,
in-depth, how statistical filtering is being used by
next-generation spam filters to identify and filter unwanted
messages, how spam filtering works and
how language
classification and machine learning combine to produce
remarkably accurate spam filters.
After reading Ending Spam, you'll have a complete
understanding of the mathematical approaches used by today's
spam filters as well as decoding, tokenization, various
algorithms (including Bayesian analysis and Markovian
discrimination) and the benefits of using open-source
solutions to end spam. Zdziarski interviewed creators of
many of the best spam filters and has included their
insights in this revealing examination of the anti-spam
crusade.
If you're a programmer designing a new spam filter, a
network admin implementing a spam-filtering solution, or
just someone who's curious about how spam filters work and
the tactics spammers use to evade them, Ending Spam will
serve as an informative analysis of the war against
spammers.
TOC
Introduction
PART I: An Introduction to Spam Filtering
Chapter 1: The History of Spam
Chapter 2: Historical Approaches to Fighting Spam
Chapter 3: Language Classification Concepts
Chapter 4: Statistical Filtering Fundamentals
PART II: Fundamentals of Statistical Filtering
Chapter 5: Decoding: Uncombobulating Messages
Chapter 6: Tokenization: The Building Blocks of Spam
Chapter 7: The Low-Down Dirty Tricks of Spammers
Chapter 8: Data Storage for a Zillion Records
Chapter 9: Scaling in Large Environments
PART III: Advanced Concepts of Statistical Filtering
Chapter 10: Testing Theory
Chapter 11: Concept Identification: Advanced Tokenization
Chapter 12: Fifth-Order Markovian Discrimination
Chapter 13: Intelligent Feature Set Reduction
Chapter 14: Collaborative Algorithms
Appendix: Shining Examples of Filtering
Index
The history of Spam
Historical approaches to fighting Spam
Language classification concepts
Statistical filtering fundamentals
Decoding
Tokenization
Tricks of Spammers
Data storage for a zillion records
Scaling in large environments
Testing theory
Concept identification
Fifth order Markovian discrimination
Intelligent feature set reduction
Collaborative algorithms
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