The Elements of Statistical Learning (Record no. 59233)

MARC details
000 -LEADER
fixed length control field 04730nam a22005655i 4500
001 - CONTROL NUMBER
control field 978-0-387-84858-7
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251006084423.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 100301s2009 xxu| s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780387848587
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 99780387848587
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-0-387-84858-7
Source of number or code doi
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.5
Edition information 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Hastie, Trevor.
Relator term author.
245 14 - TITLE STATEMENT
Title The Elements of Statistical Learning
Medium [electronic resource] :
Remainder of title Data Mining, Inference, and Prediction /
Statement of responsibility, etc. by Trevor Hastie, Robert Tibshirani, Jerome Friedman.
250 ## - EDITION STATEMENT
Edition statement Second Edition.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture New York, NY :
Name of producer, publisher, distributor, manufacturer Springer New York :
-- Imprint: Springer,
Date of production, publication, distribution, manufacture, or copyright notice 2009.
300 ## - PHYSICAL DESCRIPTION
Extent XXII, 745 p. 282 illus.
Other physical details online resource.
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
347 ## - DIGITAL FILE CHARACTERISTICS
File type text file
Encoding format PDF
Source rda
490 1# - SERIES STATEMENT
Series statement Springer Series in Statistics,
International Standard Serial Number 0172-7397
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 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.
520 ## - SUMMARY, ETC.
Summary, etc. 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.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element STATISTICS.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element DATA MINING.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element ARTIFICIAL INTELLIGENCE.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element BIOINFORMATICS.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element BIOLOGY
General subdivision DATA PROCESSING.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MATHEMATICAL STATISTICS.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element STATISTICS.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element STATISTICAL THEORY AND METHODS.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element DATA MINING AND KNOWLEDGE DISCOVERY.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element COMPUTATIONAL BIOLOGY/BIOINFORMATICS.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element ARTIFICIAL INTELLIGENCE (INCL. ROBOTICS).
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element COMPUTER APPL. IN LIFE SCIENCES.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element STATISTICS FOR ENGINEERING, PHYSICS, COMPUTER SCIENCE, CHEMISTRY AND EARTH SCIENCES.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Tibshirani, Robert.
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Friedman, Jerome.
Relator term author.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9780387848570
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Springer Series in Statistics,
International Standard Serial Number 0172-7397
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://dx.doi.org/10.1007/978-0-387-84858-7">http://dx.doi.org/10.1007/978-0-387-84858-7</a>
Public note Ver el texto completo en las instalaciones del CICY
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-- ZDB-2-SMA
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Libros electrónicos
Holdings
Lost status Source of classification or shelving scheme Damaged status Not for loan Collection Home library Current library Shelving location Date acquired Total checkouts Full call number Date last seen Price effective from Koha item type
  Dewey Decimal Classification     Libro electrónico CICY CICY Libro electrónico 06.10.2025   519.5 06.10.2025 06.10.2025 Libros electrónicos