Central Library

The Elements of Statistical Learning (Record no. 81662)

000 -LEADER
fixed length control field 05052nam a22006255i 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 20221102211537.0
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
-- 978-0-387-84858-7
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-0-387-84858-7
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q334-342
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA347.A78
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM004000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Hastie, Trevor.
Relator term author.
Relationship aut
-- http://id.loc.gov/vocabulary/relators/aut
9 (RLIN) 66506
245 14 - TITLE STATEMENT
Title The Elements of Statistical Learning
Medium [electronic resource] :
Remainder of title Data Mining, Inference, and Prediction, Second Edition /
Statement of responsibility, etc. by Trevor Hastie, Robert Tibshirani, Jerome Friedman.
250 ## - EDITION STATEMENT
Edition statement 2nd ed. 2009.
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. 658 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 2197-568X
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 Artificial intelligence.
9 (RLIN) 3407
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data mining.
9 (RLIN) 3907
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Probabilities.
9 (RLIN) 4604
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Statistics .
9 (RLIN) 31616
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Bioinformatics.
9 (RLIN) 9561
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Artificial Intelligence.
9 (RLIN) 3407
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data Mining and Knowledge Discovery.
9 (RLIN) 66507
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Probability Theory.
9 (RLIN) 17950
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Statistical Theory and Methods.
9 (RLIN) 31618
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computational and Systems Biology.
9 (RLIN) 31619
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Tibshirani, Robert.
Relator term author.
Relationship aut
-- http://id.loc.gov/vocabulary/relators/aut
9 (RLIN) 66508
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Friedman, Jerome.
Relator term author.
Relationship aut
-- http://id.loc.gov/vocabulary/relators/aut
9 (RLIN) 66509
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
9 (RLIN) 66510
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9780387848846
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9780387848570
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9781071621226
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Springer Series in Statistics,
International Standard Serial Number 2197-568X
9 (RLIN) 66511
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-0-387-84858-7">https://doi.org/10.1007/978-0-387-84858-7</a>
912 ## -
-- ZDB-2-SMA
912 ## -
-- ZDB-2-SXMS
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eTextbook

No items available.