2009, ISBN: 9780387848570
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2009, ISBN: 9780387848570
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2009, ISBN: 9780387848570
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2009, ISBN: 9780387848570
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics) - gebunden oder broschiert
2008, ISBN: 9780387848570
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booklooker.de |
2009, ISBN: 9780387848570
Original Hardcover. Publisher: Springer International Publish | Utg. 2009 | Original Hardcover | 745 p. | This book is brand new. | Series: Springer series in statistics | Language: Enge… Mehr…
2009, ISBN: 9780387848570
[ED: Buch], [PU: Springer-Verlag New York Inc.], Neuware - This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common con… Mehr…
2009
ISBN: 9780387848570
[ED: Buch], [PU: Springer-Verlag New York Inc.], Neuware - This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common con… Mehr…
2009, ISBN: 9780387848570
[ED: Buch], [PU: Springer-Verlag New York Inc.], Neuware - This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common con… Mehr…
The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics) - gebunden oder broschiert
2008, ISBN: 9780387848570
[ED: Gebundene Ausgabe], [PU: Springer], GB, [SC: 10.00], Neuware, gewerbliches Angebot, 9.30x6.30x1.60 zoll, 744, [GW: 1409g], 2nd, PayPal, Internationaler Versand
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Detailangaben zum Buch - The Elements of Statistical Learning
EAN (ISBN-13): 9780387848570
ISBN (ISBN-10): 0387848576
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2009
Herausgeber: Springer-Verlag New York Inc.
768 Seiten
Gewicht: 1,429 kg
Sprache: eng/Englisch
Buch in der Datenbank seit 2008-09-24T19:38:07+02:00 (Berlin)
Detailseite zuletzt geändert am 2024-04-18T21:04:25+02:00 (Berlin)
ISBN/EAN: 9780387848570
ISBN - alternative Schreibweisen:
0-387-84857-6, 978-0-387-84857-0
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: trevor hastie, friedman, jerome, robert friedmann, rob, author, robert ris, röber, fried
Titel des Buches: the four elements, learning from the least, learning how ask, the elements statistical learning data mining inference and prediction second edition springer series statistics, elemen, jerome
Daten vom Verlag:
Autor/in: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Titel: Springer Series in Statistics; The Elements of Statistical Learning - Data Mining, Inference, and Prediction, Second Edition
Verlag: Springer; Springer US
745 Seiten
Erscheinungsjahr: 2009-02-09
New York; NY; US
Sprache: Englisch
80,24 € (DE)
82,49 € (AT)
88,50 CHF (CH)
Available
XXII, 745 p. 658 illus., 604 illus. in color.
BB; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Averaging; Boosting; Projection pursuit; Random Forest; Support Vector Machine; classification; clustering; data mining; machine learning; supervised learning; unsupervised learning; Artificial Intelligence; Data Mining and Knowledge Discovery; Probability Theory; Statistical Theory and Methods; Computational and Systems Biology; Data Mining; Wissensbasierte Systeme, Expertensysteme; Wahrscheinlichkeitsrechnung und Statistik; Stochastik; DV-gestützte Biologie/Bioinformatik; BB; EA; BA
Overview of Supervised Learning.- Linear Methods for Regression.- Linear Methods for Classification.- Basis Expansions and Regularization.- Kernel Smoothing Methods.- Model Assessment and Selection.- Model Inference and Averaging.- Additive Models, Trees, and Related Methods.- Boosting and Additive Trees.- Neural Networks.- Support Vector Machines and Flexible Discriminants.- Prototype Methods and Nearest-Neighbors.- Unsupervised Learning.- Random Forests.- Ensemble Learning.- Undirected Graphical Models.- High-Dimensional Problems: p ? N.The many topics include neural networks, support vector machines, classification trees and boosting - the first comprehensive treatment of this topic in any book Includes more than 200 pages of four-color graphics Includes supplementary material: sn.pub/extras
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