Ensemble learning [electronic resource] : pattern classification using ensemble methods / Lior Rokach.
By: Rokach, Lior
.
Material type: 
Computer fileSeries: Series in machine perception and artificial intelligence: v. 85.Publisher: Singapore : World Scientific Publishing Co. Pte Ltd., ©2019Edition: 2nd ed.Description: 1 online resource (300 p.) : ill.ISBN: 9789811201967.Other title: Pattern classification using ensemble methods.Subject(s): Pattern recognition systems | Algorithms | Machine learningGenre/Form: Electronic books.DDC classification: 621.389/28 Online resources: Access to full text is restricted to subscribers. Summary: "This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced. Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized. The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods."-- Publisher's website.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Title from web page (viewed February 28, 2019)
2010 ed. entitled: Pattern classification using ensemble methods.
Includes bibliographical references and index.
"This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced. Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized. The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods."-- Publisher's website.
There are no comments for this item.