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200 1 $aChurch and community$e1200-1600$estudies in the history of Florence and New Spain$fRichard C. Trexler
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200 10$aEnding spam$b[electronic resource] $eBayesian content filtering and the art of statistical language classification /$fJonathan A. Zdziarski
205   $a1st ed.
210   $aSan Francisco $cNo Starch Press$d2005
215   $a1 online resource (314 p.)
300   $aIncludes index.
311   $a1-59327-052-6 
327   $aPreliminaries; Acknowledgments; Brief Contents; Contents In Detail; Introduction; The History Of Spam; Historical Approaches To Fighting Spam; Language Classification Concepts; Statistical Filtering Fundamentals; Decoding: Uncombobulating Messages; Tokenization: The Building Blocks Of Spam; The Low-down Dirty Tricks Of Spammers; Data Storage For A Zillion Records; Scaling In Large Environments; Testing Theory; Concept Identification: Advanced Tokenization; Fifth-order Markovian Discrimination; Intelligent Feature Set Reduction; Collaborative Algorithms; Shining Examples Of Filtering; Index
330   $aEnding Spam describes, in-depth, how statistical filtering is being used by next-generation spam filters to identify and filter unwanted email. Readers gain a complete understanding of the mathematical approaches used in today's spam filters, decoding, tokenization, the use of various algorithms (including Bayesian analysis and Markovian discrimination), and the benefits of using open source solutions to end spam. 
606   $aSpam filtering (Electronic mail)
606   $aFilters (Mathematics)
615  0$aSpam filtering (Electronic mail)
615  0$aFilters (Mathematics)
676   $a005.7/13
700   $aZdziarski$b Jonathan A$01463487
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