Read Recent Advances in Ensembles for Feature Selection (Intelligent Systems Reference Library Book 147)

Recent Advances in Ensembles for Feature Selection (Intelligent Systems Reference Library Book 147)

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Description:This book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance.With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative.The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges that researchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Recent Advances in Ensembles for Feature Selection (Intelligent Systems Reference Library Book 147). To get started finding Recent Advances in Ensembles for Feature Selection (Intelligent Systems Reference Library Book 147), you are right to find our website which has a comprehensive collection of manuals listed.
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ISBN: 3319900803

Description: This book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance.With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative.The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges that researchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Recent Advances in Ensembles for Feature Selection (Intelligent Systems Reference Library Book 147). To get started finding Recent Advances in Ensembles for Feature Selection (Intelligent Systems Reference Library Book 147), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Recent Advances in Ensembles for Feature Selection (Intelligent Systems Reference Library Book 147)

Recent Advances in Ensembles for Feature Selection (Intelligent Systems Reference Library Book 147)

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