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Ending Spam

Bayesian Content Filtering and the Art of Statistical Language Classification

Ending Spam Image

Book details:

Publisher:No Starch Press
Category: No Starch
Author:
Edition:1
ISBN-10:1593270526
ISBN-13:9781593270520
Pages:312
Published:
Posted:Jun 30 2016
Language:English
Book format:CHM
Book size:2.01 MB

Book 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.TOCIntroductionPART I: An Introduction to Spam FilteringChapter 1: The History of SpamChapter 2: Historical Approaches to Fighting SpamChapter 3: Language Classification ConceptsChapter 4: Statistical Filtering FundamentalsPART 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 EnvironmentsPART 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 AlgorithmsAppendix: Shining Examples of FilteringIndex

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