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Idioma: Inglés
Publicado por Springer Nature Switzerland AG, CH, 2019
ISBN 10: 3030079295 ISBN 13: 9783030079291
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Añadir al carritoPaperback. Condición: New. Softcover Reprint of the Original 1st 2018 ed.
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Publicado por Springer International Publishing, 2019
ISBN 10: 3030079295 ISBN 13: 9783030079291
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Añadir al carritoCondición: 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 .
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Añadir al carritoTaschenbuch. Condición: 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.
Idioma: Inglés
Publicado por Springer International Publishing, Springer International Publishing, 2019
ISBN 10: 3030079295 ISBN 13: 9783030079291
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Añadir al carritoTaschenbuch. Condición: 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 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.
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Publicado por Springer Nature Switzerland AG, CH, 2019
ISBN 10: 3030079295 ISBN 13: 9783030079291
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Añadir al carritoPaperback. Condición: New. Softcover Reprint of the Original 1st 2018 ed.
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Publicado por Springer-Verlag New York Inc, 2019
ISBN 10: 3030079295 ISBN 13: 9783030079291
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Añadir al carritoPaperback. Condición: Brand New. reprint edition. 219 pages. 9.25x6.10x0.52 inches. In Stock. This item is printed on demand.
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Publicado por Springer International Publishing Jan 2019, 2019
ISBN 10: 3030079295 ISBN 13: 9783030079291
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoTaschenbuch. Condición: 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 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. 220 pp. Englisch.
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Añadir al carritoCondición: New. PRINT ON DEMAND pp. 220.
Idioma: Inglés
Publicado por Springer, Palgrave Macmillan Jan 2019, 2019
ISBN 10: 3030079295 ISBN 13: 9783030079291
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Añadir al carritoTaschenbuch. Condición: 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 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 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.
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Añadir al carritoCondición: New. Print on Demand pp. 220.