02563nam 2200541Ia 450 991078339850332120230617031739.01-59327-085-2(CKB)1000000000027050(EBL)273487(OCoLC)191037099(SSID)ssj0000074729(PQKBManifestationID)11110145(PQKBTitleCode)TC0000074729(PQKBWorkID)10144199(PQKB)10688917(MiAaPQ)EBC273487(Au-PeEL)EBL273487(CaPaEBR)ebr10082413(OCoLC)70745817(EXLCZ)99100000000002705020050322d2005 uy 0engur|n|---|||||txtccrEnding spam[electronic resource] Bayesian content filtering and the art of statistical language classification /Jonathan A. Zdziarski1st ed.San Francisco No Starch Press20051 online resource (314 p.)Includes index.1-59327-052-6 Preliminaries; 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; IndexEnding 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. Spam filtering (Electronic mail)Filters (Mathematics)Spam filtering (Electronic mail)Filters (Mathematics)005.7/13Zdziarski Jonathan A1463487MiAaPQMiAaPQMiAaPQBOOK9910783398503321Ending spam3672760UNINA