Feature Selection for Data and Pattern Recognition (Studies in Computational Intelligence, 584, Band 584) [Hardcover] Sta?czyk
Language: English
Published by Springer, 2015
Series: Book 105 of 538 - Studies in Computational Intelligence
- Hardcover
- Used
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- Title
- Feature Selection for Data and Pattern Recognition (Studies in Computational Intelligence, 584, Band 584) [Hardcover] Sta?czyk
- Author
- Stanczyk
- Publisher
- Springer
- Publication year
- 2015
- Condition
- As New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3662456192
- ISBN 13
- 9783662456194
- Series
- Book 105 of 538: Studies in Computational Intelligence
This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition.
Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks.
This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies.
"Synopsis" may belong to another edition of this title.
From the Back Cover
This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition.
Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks.
This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies.
"About the title" may belong to another edition of this title.
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