Machine Learning for Email: Spam Filtering and Priority Inbox - Softcover

Conway, Drew

 
9781449314309: Machine Learning for Email: Spam Filtering and Priority Inbox
Softcover
ISBN 10: 1449314309 ISBN 13: 9781449314309
Verlag: O'Reilly Media, 2011

Inhaltsangabe

If you’re an experienced programmer willing to crunch data, this concise guide will show you how to use machine learning to work with email. You’ll learn how to write algorithms that automatically sort and redirect email based on statistical patterns. Authors Drew Conway and John Myles White approach the process in a practical fashion, using a case-study driven approach rather than a traditional math-heavy presentation.

This book also includes a short tutorial on using the popular R language to manipulate and analyze data. You’ll get clear examples for analyzing sample data and writing machine learning programs with R.

Mine email content with R functions, using a collection of sample files Analyze the data and use the results to write a Bayesian spam classifier Rank email by importance, using factors such as thread activity Use your email ranking analysis to write a priority inbox program Test your classifier and priority inbox with a separate email sample set

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Über die Autorin bzw. den Autor

Drew Conway is a PhD candidate in Politics at NYU. He studies international relations, conflict, and terrorism using the tools of mathematics, statistics, and computer science in an attempt to gain a deeper understanding of these phenomena. His academic curiosity is informed by his years as an analyst in the U.S. intelligence and defense communities.

John Myles White is a Ph.D. student in the Princeton Psychology Department, where he studies how humans make decisions both theoretically and experimentally. Outside of academia, John has been heavily involved in the data science movement, which has pushed for an open source software approach to data analysis. He is also the leadmaintainer for several popular R packages, including ProjectTemplate and log4r.

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Verlag
O'Reilly Media
Erscheinungsdatum
2011
Sprache
Englisch
ISBN 10 
1449314309
ISBN 13 
9781449314309
Einband
Taschenbuch
Anzahl der Seiten
148
Kontakt zum Hersteller
O'Reilly
https://oreilly.com

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