The elements of statistical learning : data mining, inference, and prediction /

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Bibliographic Details
Author / Creator:Hastie, Trevor.
Edition:2nd ed.
Imprint:New York : Springer, c2009.
Description:1 online resource (xxii, 745 p.) : ill.
Language:English
Series:Springer series in statistics
Springer series in statistics.
Subject:Supervised learning (Machine learning)
Computer Science -- IT.
COMPUTERS -- Database Management -- Data Mining.
Estatística computacional.
Estatistica.
Mineração de dados.
Inferencia Estatistica.
Maschinelles Lernen.
Statistik.
Supervised learning (Machine learning)
Electronic data processing.
Statistics.
Biology -- Data processing.
Computational biology.
Mathematics -- Data processing.
Data mining.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/8889442
Hidden Bibliographic Details
Other authors / contributors:Tibshirani, Robert.
Friedman, J. H. (Jerome H.)
ISBN:9780387848587
0387848584
9780387848570
0387848576
9786612126741
6612126744
Notes:Includes bibliographical references and indexes.
Description based on print version record.
Summary:"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."--Jacket.
Other form:Print version: Hastie, Trevor. Elements of statistical learning. 2nd ed. New York : Springer, c2009 9780387848570 0387848576