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

@book{citeulike:854531, abstract = {{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> 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> TOC Introduction</p> <p> 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</p> <p> 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</p> <p> 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</p> <p> Appendix: Shining Examples of Filtering</p> <p> Index</p>}}, author = {Zdziarski, Jonathan A.}, citeulike-article-id = {854531}, citeulike-linkout-0 = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&amp;path=ASIN/1593270526}, citeulike-linkout-1 = {http://www.amazon.de/exec/obidos/redirect?tag=citeulike01-21&amp;path=ASIN/1593270526}, citeulike-linkout-2 = {http://www.amazon.fr/exec/obidos/redirect?tag=citeulike06-21&amp;path=ASIN/1593270526}, citeulike-linkout-3 = {http://www.amazon.co.uk/exec/obidos/ASIN/1593270526/citeulike00-21}, citeulike-linkout-4 = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/1593270526}, citeulike-linkout-5 = {http://www.worldcat.org/isbn/1593270526}, citeulike-linkout-6 = {http://books.google.com/books?vid=ISBN1593270526}, citeulike-linkout-7 = {http://www.amazon.com/gp/search?keywords=1593270526&index=books&linkCode=qs}, citeulike-linkout-8 = {http://www.librarything.com/isbn/1593270526}, day = {01}, howpublished = {Paperback}, isbn = {1593270526}, keywords = {classify, filter, spam, statistic, usenet}, month = {July}, posted-at = {2006-09-22 17:19:28}, priority = {2}, publisher = {{No Starch Press}}, title = {Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification}, url = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/1593270526}, year = {2005} }

TY - BOOK ID - citeulike:854531 L3 - citeulike-article-id:854531 N2 - {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> 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> TOC Introduction</p> <p> 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</p> <p> 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</p> <p> 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</p> <p> Appendix: Shining Examples of Filtering</p> <p> Index</p>} SN - 1593270526 TI - Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification PB - {No Starch Press} KW - classify KW - filter KW - spam KW - statistic KW - usenet AU - Zdziarski, Jonathan PY - 2005/07/01/ UR - http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&amp;path=ASIN/1593270526 ER -