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Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Langue: anglais
Edité par Springer International Publishing, Springer Nature Switzerland, 2019
ISBN 10 : 3030139611 ISBN 13 : 9783030139612
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 181,89
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Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who dealwith stream data, e.g. in telecommunication, banking, and sensor networks.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierHardcover. Etat : Brand New. 330 pages. 9.50x6.50x0.75 inches. In Stock.
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Ajouter au panierHardcover. Etat : New. New. book.
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Vendeur : Brook Bookstore On Demand, Napoli, NA, Italie
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Ajouter au panierEtat : new. Questo è un articolo print on demand.
Langue: anglais
Edité par Springer International Publishing, 2019
ISBN 10 : 3030139611 ISBN 13 : 9783030139612
Vendeur : moluna, Greven, Allemagne
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Ajouter au panierGebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents a unique and innovative approach to stream data miningUnlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justifiedIs intended for a pr.
Langue: anglais
Edité par Springer International Publishing Mrz 2019, 2019
ISBN 10 : 3030139611 ISBN 13 : 9783030139612
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 181,89
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Ajouter au panierBuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who dealwith stream data, e.g. in telecommunication, banking, and sensor networks. 340 pp. Englisch.
Langue: anglais
Edité par Springer International Publishing, Springer Nature Switzerland Mär 2019, 2019
ISBN 10 : 3030139611 ISBN 13 : 9783030139612
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 181,89
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Ajouter au panierBuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who dealwith stream data, e.g. in telecommunication, banking, and sensor networks.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 340 pp. Englisch.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
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Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Ajouter au panierEtat : New. PRINT ON DEMAND.