Data Mining Practical Machine Learning Tools and Techniques

Name: Data Mining Practical Machine Learning Tools and Techniques
Price: 4.2 USD
Availability: InStock
ISBN: 9780120884070

ISBN-10: 0120884070

ISBN-13: 9780120884070

Edition: 2nd 2005 (Revised)

Authors: Ian H. Witten, Eibe Frank

List price: $75.95

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As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly…

Book details

List price: $75.95
Edition: 2nd
Copyright year: 2005
Publisher: Elsevier Science & Technology Books
Publication date: 6/8/2005
Binding: Paperback
Pages: 560
Size: 7.50" wide x 9.25" long x 1.50" tall
Weight: 2.486
Language: English

Eibe Frank is a researcher in the Machine Learning group at the University of Waikato. He holds a degree in computer science from the University of Karlsruhe in Germany and is the author of several papers, both presented at machine learning conferences and published in machine learning journals.




Whats it all about?



Input


Concepts, instances, attributes



Output


Knowledge representation



Algorithms


The basic methods



Credibility


Evaluating whats been learned



Implementations


Real machine learning schemes



Transformations


Engineering the input and output



Moving on


Extensions and applications



The Weka machine learning workbench



Introduction to Weka



The Explorer



The Knowledge Flow interface



The Experimenter



The command-line interface



Embedded machine learning



Writing new learning schemes


References


Index