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Springer Series in Statistics

The Elements of Statistical Learning

Data Mining, Inference, and Prediction, Second Edition

Autoren: Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome

Vorschau
  • 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
Weitere Vorteile

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eBook 64,19 €
Preis für Deutschland (Brutto)
  • ISBN 978-0-387-84858-7
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Hardcover 80,24 €
Preis für Deutschland (Brutto)
  • ISBN 978-0-387-84857-0
  • Kostenfreier Versand für Individualkunden weltweit
  • Kostenloser Sofortzugriff, sofern verfügbar* auf die eBook-Version bei jeder Buchbestellung
  • Versandfertig innerhalb von 3 Tagen.
Über dieses Lehrbuch

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Über die Autor*innen

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Inhaltsverzeichnis (18 Kapitel)

Inhaltsverzeichnis (18 Kapitel)
  • Introduction

    Seiten 1-8

    Hastie, Trevor (et al.)

  • Overview of Supervised Learning

    Seiten 9-41

    Hastie, Trevor (et al.)

  • Linear Methods for Regression

    Seiten 43-99

    Hastie, Trevor (et al.)

  • Linear Methods for Classification

    Seiten 101-137

    Hastie, Trevor (et al.)

  • Basis Expansions and Regularization

    Seiten 139-189

    Hastie, Trevor (et al.)

Dieses Buch kaufen

eBook 64,19 €
Preis für Deutschland (Brutto)
  • ISBN 978-0-387-84858-7
  • Versehen mit digitalem Wasserzeichen, DRM-frei
  • Erhältliche Formate: PDF
  • eBooks sind auf allen Endgeräten nutzbar
  • Sofortiger eBook Download nach Kauf
Hardcover 80,24 €
Preis für Deutschland (Brutto)
  • ISBN 978-0-387-84857-0
  • Kostenfreier Versand für Individualkunden weltweit
  • Kostenloser Sofortzugriff, sofern verfügbar* auf die eBook-Version bei jeder Buchbestellung
  • Versandfertig innerhalb von 3 Tagen.
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Bibliografische Information

Bibliographic Information
Buchtitel
The Elements of Statistical Learning
Buchuntertitel
Data Mining, Inference, and Prediction, Second Edition
Autoren
Titel der Buchreihe
Springer Series in Statistics
Copyright
2009
Verlag
Springer-Verlag New York
Copyright Inhaber
Springer Science+Business Media, LLC, part of Springer Nature
Vertriebsrechte
Distribution rights for India: Mehul Book Sales, Mumbai, India
eBook ISBN
978-0-387-84858-7
DOI
10.1007/978-0-387-84858-7
Hardcover ISBN
978-0-387-84857-0
Buchreihen ISSN
0172-7397
Auflage
2
Seitenzahl
XXII, 745
Anzahl der Bilder
658 schwarz-weiß Abbildungen
Themen

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