9783030079291 - recent advances in ensembles for feature selection: 147 di bolón-canedo, verónica; alonso-betanzos, amparo (21 risultati)
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Paperback. Condizione: New. 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 evalua…ting 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 thatresearchers 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. Softcover Reprint of the Original 1st 2018 ed.
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Condizione: New. Offers a comprehensive overview of ensemble learning in the field of feature selection (FS)Provides the user with the background and tools needed to develop new ensemble methods for feature selectionReviews various techniques for combining .
Altre immaginiLingua: Inglese
Editore: Springer, 2019
Serie: Libro 117 di 188 - Intelligent Systems Reference Library
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Taschenbuch. Condizione: Neu. Recent Advances in Ensembles for Feature Selection | Verónica Bolón-Canedo (u. a.) | Taschenbuch | Intelligent Systems Reference Library | xiv | Englisch | 2019 | Springer | EAN 9783030079291 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]…hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 co…mbining 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 thatresearchers 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.
Lingua: Inglese
Editore: Springer Nature Switzerland AG, CH, 2019
Serie: Libro 117 di 188 - Intelligent Systems Reference Library
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Paperback. Condizione: New. 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 evalua…ting 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 thatresearchers 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. Softcover Reprint of the Original 1st 2018 ed.
Lingua: Inglese
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Paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Paperback. Condizione: Brand New. reprint edition. 219 pages. 9.25x6.10x0.52 inches. In Stock. This item is printed on demand.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 t…echniques 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. 220 pp. Englisch.
Lingua: Inglese
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Condizione: New. Print on Demand pp. 220.
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Condizione: New. PRINT ON DEMAND pp. 220.
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 techn…iques 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 thatresearchers 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 220 pp. Englisch.
