Ending Spam
Bayesian Content Filtering and the Art of Statistical Language Classification
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Estimated delivery time: Expected time of arrival: end of January 2026.
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Product details:
- Edition number 1
- Publisher No Starch Press
- Date of Publication 28 June 2005
- Number of Volumes Print PDF
- ISBN 9781593270520
- Binding Paperback
- No. of pages312 pages
- Size 234x177 mm
- Weight 508 g
- Language English 0
Categories
Short description:
Explains how spam works, how network administrators can implement spam filters, or how programmers can develop new remarkably accurate filters using language classification and machine learning. Original. (Advanced)
MoreLong description:
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.
TOCIntroduction
PART I: An Introduction to Spam FilteringChapter 1: The History of SpamChapter 2: Historical Approaches to Fighting SpamChapter 3: Language Classification ConceptsChapter 4: Statistical Filtering Fundamentals
PART II: Fundamentals of Statistical FilteringChapter 5: Decoding: Uncombobulating MessagesChapter 6: Tokenization: The Building Blocks of SpamChapter 7: The Low-Down Dirty Tricks of SpammersChapter 8: Data Storage for a Zillion RecordsChapter 9: Scaling in Large Environments
PART III: Advanced Concepts of Statistical FilteringChapter 10: Testing TheoryChapter 11: Concept Identification: Advanced TokenizationChapter 12: Fifth-Order Markovian DiscriminationChapter 13: Intelligent Feature Set ReductionChapter 14: Collaborative Algorithms
Appendix: Shining Examples of Filtering
Index
