Ending spam Bayesian content filtering and the art of statistical language classification
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| Format: | Buch |
| Sprache: | English |
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San Francisco
No Starch Press
2005
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| 245 | 1 | 0 | |a Ending spam |b Bayesian content filtering and the art of statistical language classification |c by Jonathan A. Zdziarski |
| 264 | 1 | |a San Francisco |b No Starch Press |c 2005 | |
| 300 | |a XX, 287 S. |b Ill. | ||
| 336 | |b txt |2 rdacontent | ||
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| 650 | 4 | |a Spam filtering (Electronic mail) | |
| 650 | 4 | |a Filters (Mathematics) | |
| 650 | 4 | |a Spam filtering (Electronic mail) |x Computer programs | |
| 650 | 4 | |a Electronic mail systems |x Security measures | |
| 650 | 4 | |a Spam (Electronic mail) |x Prevention | |
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Datensatz im Suchindex
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|---|---|
| adam_text | BRIEF CONTENTS
Introduction.................................................................................................................
xvii
PART I: An Introduction to Spam Filtering
Chapter
1 :
The History of Spam
........................................................................................3
Chapter
2:
Historical Approaches to Fighting Spam
..........................................................25
Chapter
3:
Language Classification Concepts
...................................................................45
Chapter
4:
Statistical Filtering Fundamentals
.....................................................................63
PART II: Fundamentals of Statistical Filtering
Chapter
5:
Decoding: Uncombobulating Messages
...........................................................87
Chapter
6:
Tokenization: The Building Blocks of Spam
.......................................................97
Chapter
7:
The Low-Down Dirty Tricks of Spammers
........................................................
Ill
Chapter
8:
Data Storage for a Zillion Records
................................................................141
Chapter
9:
Scaling in Large Environments
......................................................................157
PART III: Advanced Concepts of Statistical Filtering
Chapter
10:
Testing Theory
..........................................................................................177
Chapter
11 :
Concept Identification: Advanced Tokenization
............................................197
Chapter
12:
Fifth-Order Markovian Discrimination
..........................................................215
Chapter
13:
Intelligent Feature Set Reduction
..................................................................227
Chapter
14:
Collaborative Algorithms
...........................................................................241
Appendix: Shining Examples of Filtering
........................................................................257
Index
.........................................................................................................................275
Join author Jonathan 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
¡ust
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.
|
| adam_txt |
BRIEF CONTENTS
Introduction.
xvii
PART I: An Introduction to Spam Filtering
Chapter
1 :
The History of Spam
.3
Chapter
2:
Historical Approaches to Fighting Spam
.25
Chapter
3:
Language Classification Concepts
.45
Chapter
4:
Statistical Filtering Fundamentals
.63
PART II: Fundamentals of Statistical Filtering
Chapter
5:
Decoding: Uncombobulating Messages
.87
Chapter
6:
Tokenization: The Building Blocks of Spam
.97
Chapter
7:
The Low-Down Dirty Tricks of Spammers
.
Ill
Chapter
8:
Data Storage for a Zillion Records
.141
Chapter
9:
Scaling in Large Environments
.157
PART III: Advanced Concepts of Statistical Filtering
Chapter
10:
Testing Theory
.177
Chapter
11 :
Concept Identification: Advanced Tokenization
.197
Chapter
12:
Fifth-Order Markovian Discrimination
.215
Chapter
13:
Intelligent Feature Set Reduction
.227
Chapter
14:
Collaborative Algorithms
.241
Appendix: Shining Examples of Filtering
.257
Index
.275
Join author Jonathan 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
¡ust
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. |
| any_adam_object | 1 |
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| spelling | Zdziarski, Jonathan Verfasser aut Ending spam Bayesian content filtering and the art of statistical language classification by Jonathan A. Zdziarski San Francisco No Starch Press 2005 XX, 287 S. Ill. txt rdacontent n rdamedia nc rdacarrier Spam filtering (Electronic mail) Filters (Mathematics) Spam filtering (Electronic mail) Computer programs Electronic mail systems Security measures Spam (Electronic mail) Prevention Mail-Filter (DE-588)4792631-4 gnd rswk-swf Mail-Filter (DE-588)4792631-4 s DE-604 Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016474497&sequence=000013&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016474497&sequence=000014&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Zdziarski, Jonathan Ending spam Bayesian content filtering and the art of statistical language classification Spam filtering (Electronic mail) Filters (Mathematics) Spam filtering (Electronic mail) Computer programs Electronic mail systems Security measures Spam (Electronic mail) Prevention Mail-Filter (DE-588)4792631-4 gnd |
| subject_GND | (DE-588)4792631-4 |
| title | Ending spam Bayesian content filtering and the art of statistical language classification |
| title_auth | Ending spam Bayesian content filtering and the art of statistical language classification |
| title_exact_search | Ending spam Bayesian content filtering and the art of statistical language classification |
| title_exact_search_txtP | Ending spam Bayesian content filtering and the art of statistical language classification |
| title_full | Ending spam Bayesian content filtering and the art of statistical language classification by Jonathan A. Zdziarski |
| title_fullStr | Ending spam Bayesian content filtering and the art of statistical language classification by Jonathan A. Zdziarski |
| title_full_unstemmed | Ending spam Bayesian content filtering and the art of statistical language classification by Jonathan A. Zdziarski |
| title_short | Ending spam |
| title_sort | ending spam bayesian content filtering and the art of statistical language classification |
| title_sub | Bayesian content filtering and the art of statistical language classification |
| topic | Spam filtering (Electronic mail) Filters (Mathematics) Spam filtering (Electronic mail) Computer programs Electronic mail systems Security measures Spam (Electronic mail) Prevention Mail-Filter (DE-588)4792631-4 gnd |
| topic_facet | Spam filtering (Electronic mail) Filters (Mathematics) Spam filtering (Electronic mail) Computer programs Electronic mail systems Security measures Spam (Electronic mail) Prevention Mail-Filter |
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