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    The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics) (Hardback) By (author) Trevor Hastie, By (author) Robert Tibshirani, By (author) Jerome Friedman

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    DescriptionDuring 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 & 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.


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  • Full bibliographic data for The Elements of Statistical Learning

    Title
    The Elements of Statistical Learning
    Subtitle
    Data Mining, Inference, and Prediction
    Authors and contributors
    By (author) Trevor Hastie, By (author) Robert Tibshirani, By (author) Jerome Friedman
    Physical properties
    Format: Hardback
    Number of pages: 767
    Width: 163 mm
    Height: 236 mm
    Thickness: 41 mm
    Weight: 1,497 g
    Language
    English
    ISBN
    ISBN 13: 9780387848570
    ISBN 10: 0387848576
    Classifications

    BIC E4L: COM
    Nielsen BookScan Product Class 3: S10.2
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 01
    LC subject heading: ,
    BIC subject category V2: PBT
    LC subject heading: ,
    B&T Merchandise Category: SCI
    B&T General Subject: 710
    B&T Modifier: Academic Level: 02
    LC classification: QA
    Ingram Subject Code: MA
    Libri: I-MA
    B&T Modifier: Text Format: 06, 01
    BIC subject category V2: UYAM
    Warengruppen-Systematik des deutschen Buchhandels: 16280
    BISAC V2.8: COM014000, MAT029000
    BIC subject category V2: PSA
    Abridged Dewey: 519
    BISAC V2.8: SCI086000
    LC subject heading:
    DC22: 006.31
    LC subject heading:
    BISAC V2.8: COM004000
    LC subject heading:
    BISAC V2.8: COM021030, COM086000
    BIC subject category V2: UYQM, UNF
    LC subject heading: ,
    BISAC V2.8: COM082000
    LC subject heading:
    LC classification: QA76.9.D343, QA75.5-76.95, TJ210.2-211.495, Q334-342, QA274-274.9, QA273.A1-274.9
    LC subject heading:
    LC classification: QA276-280, QH324.2-324.25
    LC subject heading:
    Thema V1.0: PBT, UYQM, PSA, UYAM, UNF
    Edition
    2, Revised
    Edition statement
    2nd ed. 2009. Corr. 7th printing 2013
    Illustrations note
    282 black & white illustrations, biography
    Publisher
    Springer-Verlag New York Inc.
    Imprint name
    Springer-Verlag New York Inc.
    Publication date
    09 February 2009
    Publication City/Country
    New York, NY
    Author Information
    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.
    Review quote
    From the reviews: "Like the first edition, the current one is a welcome edition to researchers and academicians equally... Almost all of the chapters are revised... The Material is nicely reorganized and repackaged, with the general layout being the same as that of the first edition... If you bought the first edition, I suggest that you buy the second editon for maximum effect, and if you haven't, then I still strongly recommend you have this book at your desk. Is it a good investment, statistically speaking!" (Book Review Editor, Technometrics, August 2009, VOL. 51, NO. 3) From the reviews of the second edition: "This second edition pays tribute to the many developments in recent years in this field, and new material was added to several existing chapters as well as four new chapters ... were included. ... These additions make this book worthwhile to obtain ... . In general this is a well written book which gives a good overview on statistical learning and can be recommended to everyone interested in this field. The book is so comprehensive that it offers material for several courses." (Klaus Nordhausen, International Statistical Review, Vol. 77 (3), 2009) "The second edition ... features about 200 pages of substantial new additions in the form of four new chapters, as well as various complements to existing chapters. ... the book may also be of interest to a theoretically inclined reader looking for an entry point to the area and wanting to get an initial understanding of which mathematical issues are relevant in relation to practice. ... this is a welcome update to an already fine book, which will surely reinforce its status as a reference." (Gilles Blanchard, Mathematical Reviews, Issue 2012 d) "The book would be ideal for statistics graduate students ... . This book really is the standard in the field, referenced in most papers and books on the subject, and it is easy to see why. The book is very well written, with informative graphics on almost every other page. It looks great and inviting. You can flip the book open to any page, read a sentence or two and be hooked for the next hour or so." (Peter Rabinovitch, The Mathematical Association of America, May, 2012)
    Back cover copy
    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.
    Table of contents
    Introduction.- 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.