Recent Advances in Ensembles for Feature Selection (Intelligent Systems Reference Library, Band 147)
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Bester Preis: Fr. 6.80 (€ 6.95)¹ (vom 03.12.2019)1
Recent Advances in Ensembles for Feature Selection
~EN HC NWISBN: 9783319900797 bzw. 331990079X, vermutlich in Englisch, Springer Shop, gebundenes Buch, neu.
Lieferung aus: Mexiko, Lagernd, zzgl. Versandkosten.
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. Hard cover.
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. Hard cover.
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Recent Advances in Ensembles for Feature Selection
~EN HC NWISBN: 9783319900797 bzw. 331990079X, vermutlich in Englisch, Springer-Verlag Gmbh, gebundenes Buch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Recent Advances in Ensembles for Feature Selection: 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. Englisch, Buch.
Recent Advances in Ensembles for Feature Selection: 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. Englisch, Buch.
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/ Alonso-Betanzos | Recent Advances in Ensembles for Feature Selection | Springer | 1st ed. 2018 | 2018
~EN NWISBN: 9783319900797 bzw. 331990079X, vermutlich in Englisch, Springer, neu.
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.
5
Recent Advances in Ensembles for Feature Selection (Intelligent Systems Reference Library) (2018)
~EN HC USISBN: 9783319900797 bzw. 331990079X, vermutlich in Englisch, gebundenes Buch, gebraucht.
Lieferung aus: Vereinigte Staaten von Amerika, Lagernd, zzgl. Versandkosten.
Hardcover book. Published by Springer (2018).
Hardcover book. Published by Springer (2018).
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Recent Advances in Ensembles for Feature Selection (Intelligent Systems Reference Library, Band 147) (2018)
EN HC NW FEISBN: 9783319900797 bzw. 331990079X, Band: 147, in Englisch, 193 Seiten, Springer, gebundenes Buch, neu, Erstausgabe.
Lieferung aus: Deutschland, Noch nicht erschienen. Versandkostenfrei.
Von Händler/Antiquariat, Amazon.de.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Von Händler/Antiquariat, Amazon.de.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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Recent Advances in Ensembles for Feature Selection
DE HC NWISBN: 9783319900797 bzw. 331990079X, in Deutsch, Springer International Publishing, gebundenes Buch, neu.
Lieferung aus: Vereinigte Staaten von Amerika, Lagernd, zzgl. Versandkosten.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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