Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification

Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification

• Spam
• Filtering
• Ending Spam
• Jonathan A. Zdziarski

<div><p>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.</p><p>After reading <i>Ending Spam</i>, 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. </p><p>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, <i>Ending Spam</i> will serve as an informative analysis of the war against spammers.</p><p>TOCIntroduction</p><p>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</p><p>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</p><p>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</p><p>Appendix: Shining Examples of Filtering</p><p>Index</p></div>