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Ending spam : Bayesian content filtering and the art of statistical language classification /
Ending 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 algorithm...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
San Francisco :
No Starch Press,
2005.
|
| Edición: | 1st ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Zdziarski, Jonathan A. | |
| 245 | 1 | 0 | |a Ending spam : |b Bayesian content filtering and the art of statistical language classification / |c Jonathan A. Zdziarski. |
| 250 | |a 1st ed. | ||
| 260 | |a San Francisco : |b No Starch Press, |c 2005. | ||
| 300 | |a 1 online resource (xx, 287 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 500 | |a Includes index. | ||
| 505 | 0 | |a An introduction to spam filtering concepts -- The history of spam -- Historical approaches to fighting spam -- Next generation filtering -- Shining examples of filtering -- Machine learning concepts -- Fundamentals of statistical filtering -- Statistical filtering fundamentals -- Decoding: uncombobulating messages -- Tokenization: the building blocks of spam -- Open source, OSX, and Milk Duds -- The low-down dirty details of spam -- Data storage for a zillion records -- Scaling for large-scale environments -- Advanced concepts of statistical filtering -- Concept identification: advanced tokenization -- Testing theory -- Fifth-order Markovian discrimination -- Concept identification: advanced tokenization -- Intelligent feature set reduction -- Collaborative algorithms -- Installing and using open source filters. | |
| 588 | 0 | |a Print version record. | |
| 506 | |3 Use copy |f Restrictions unspecified |2 star |5 MiAaHDL | ||
| 533 | |a Electronic reproduction. |b [Place of publication not identified] : |c HathiTrust Digital Library, |d 2010. |5 MiAaHDL | ||
| 538 | |a Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. |u http://purl.oclc.org/DLF/benchrepro0212 |5 MiAaHDL | ||
| 583 | 1 | |a digitized |c 2010 |h HathiTrust Digital Library |l committed to preserve |2 pda |5 MiAaHDL | |
| 520 | |a Ending 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. | ||
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Spam filtering (Electronic mail) | |
| 650 | 0 | |a Filters (Mathematics) | |
| 650 | 6 | |a Pourriels |x Filtrage. | |
| 650 | 6 | |a Filtres (Mathématiques) | |
| 650 | 7 | |a COMPUTERS |x Data Processing. |2 bisacsh | |
| 650 | 7 | |a Filters (Mathematics) |2 fast | |
| 650 | 7 | |a Spam filtering (Electronic mail) |2 fast | |
| 758 | |i has work: |a Ending spam (Text) |1 https://id.oclc.org/worldcat/entity/E39PCYBy9tcypMcFdVwjqxdcMX |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Zdziarski, Jonathan A. |t Ending spam. |b 1st ed. |d San Francisco : No Starch Press, 2005 |w (DLC) 2005008221 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=273487 |z Texto completo |
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