عنوان

The elements of statistical learning :

(Number (ISBN

0387848576

(Number (ISBN

0387848843

(Number (ISBN

9780387848570

(Number (ISBN

9780387848846

Erroneous ISBN

0387848584

Erroneous ISBN

9780387848587

Country Code

dnb

Number

b623560

Title Proper

The elements of statistical learning :

General Material Designation

[Book]

Other Title Information

data mining, inference, and prediction /

First Statement of Responsibility

Trevor Hastie, Robert Tibshirani, Jerome Friedman.

Edition Statement

Second edition.

Specific Material Designation and Extent of Item

xxii, 745 pages :

Other Physical Details

illustrations (some color), charts ;

Dimensions

24 cm

Series Title

Springer series in statistics,

ISSN of Series

0172-7397

Text of Note

Includes bibliographical references (pages 699-727) and indexes.

Text of Note

"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."--Publisher's description.

Biology-- Data processing.

Computational biology.

Data mining.

Electronic data processing.

Mathematics-- Data processing.

Statistics.

Supervised learning (Machine learning)

Algorithms.

Artificial Intelligence.

Computing Methodologies.

Learning.

Statistics as Topic.

Apprentissage supervisé (Intelligence artificielle)

Biology-- Data processing.

Computational biology.

Data mining.

Electronic data processing.

Estatística computacional.

Estatística.

exploration de données, manuel.

inférence statistique, manuel.

Inferência estatística.

Maschinelles Lernen

Mathematics-- Data processing.

Mineração de dados.

prévision statistique, manuel.

Statistics.

Statistik

Supervised learning (Machine learning)

s1se

Number

Edition

Class number

Book number

Class number

Class number

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Class number

Class number

Class number

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Book Number

System Code

System Code

System Code

System Code

System Code

System Code

System Code

Hastie, Trevor

Friedman, J. H., (Jerome H.)

Tibshirani, Robert

Date of Transaction

20200617082632.0

Cataloguing Rules (Descriptive Conventions))

rda

Electronic name

[Book]

Y