The elements of statistical learning: data mining, inference, and prediction
(Book)

Book Cover
Contributors:
Published:
New York : Springer, [2009].
Format:
Book
Edition:
2nd ed.
Physical Desc:
xxii, 745 pages : illustrations (some color) ; 24 cm.
Status:
Copies
Location
Call Number
Status
Last Check-In
ASU Main (3rd floor)
Q325.75 .H37 2009
In Transit
Citations
APA Citation (style guide)

Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, Springer.

Chicago / Turabian - Author Date Citation (style guide)

Hastie, Trevor, Robert, Tibshirani and J. H. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, Springer.

Chicago / Turabian - Humanities Citation (style guide)

Hastie, Trevor, Robert, Tibshirani and J. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, Springer, 2009.

MLA Citation (style guide)

Hastie, Trevor,, et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York, Springer, 2009.

Note! Citation formats are based on standards as of July 2022. Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy.
Description

"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.

Also in This Series
More Like This
More Details
Language:
English
ISBN:
9780387848570, 0387848576

Notes

Bibliography
Includes bibliographical references (pages [699]-727) and indexes.
Description
"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.
More Copies In Prospector
Loading Prospector Copies...
Staff View
Grouped Work ID:
0ff0366d-7ae2-b59b-6239-3f073aa58465
Go To GroupedWork

Record Information

Last Sierra Extract TimeSep 18, 2022 08:18:20 AM
Last File Modification TimeSep 18, 2022 08:18:41 AM
Last Grouped Work Modification TimeOct 05, 2022 07:16:45 AM

MARC Record

LEADER04694cam a2200697Ia 4500
001300478243
003OCoLC
00520140123122306.0
008090130t20092009nyua     b    001 0 eng  
010 |a 2008941148
015 |a 08,N30,0597|2 dnb
0167 |a 989494330|2 DE-101
0167 |a 014912040|2 Uk
019 |a 717787914|a 754964653
020 |a 9780387848570
020 |a 0387848576
020 |z 9780387848587 (electronic)
020 |z 0387848584 (electronic)
035 |a (OCoLC)300478243|z (OCoLC)717787914|z (OCoLC)754964653
040 |a NUI|b eng|c NUI|d YDXCP|d CTB|d CDX|d BWX|d IXA|d OHX|d OCLCQ|d OCL|d UBA|d SNK|d AUW|d DLC|d HEBIS|d DEBBG|d OCL|d DEBSZ|d CHRRO|d GZM|d MYG|d ALAUL|d UKMGB|d OCLCQ|d OHS|d FDA|d OCLCF|d CLZ
049 |a CLZA
050 4|a Q325.75|b .H37 2009
1001 |a Hastie, Trevor,|0 http://id.loc.gov/authorities/names/n90646512|e author.
24514|a The elements of statistical learning :|b data mining, inference, and prediction /|c Trevor Hastie, Robert Tibshirani, Jerome Friedman.
250 |a 2nd ed.
264 1|a New York :|b Springer,|c [2009]
264 4|c ©2009
300 |a xxii, 745 pages :|b illustrations (some color) ;|c 24 cm.
336 |a text|b txt|2 rdacontent
337 |a unmediated|b n|2 rdamedia
338 |a volume|b nc|2 rdacarrier
4901 |a Springer series in statistics,|x 0172-7397
504 |a Includes bibliographical references (pages [699]-727) and indexes.
50500|g 1.|t Introduction --|g 2.|t Overview of supervised learning --|g 3.|t Linear methods for regression --|g 4.|t Linear methods for classification --|g 5.|t Basis expansions and regularization --|g 6.|t Kernel smoothing methods --|g 7.|t Model assessment and selection --|g 8.|t Model inference and averaging --|g 9.|t Additive models, trees, and related methods --|g 10.|t Boosting and additive trees --|g 11.|t Neural networks --|g 12.|t Support vector machines and flexible discriminants --|g 13.|t Prototype methods and nearest-neighbors --|g 14.|t Unsupervised learning --|g 15.|t Random forests --|g 16.|t Ensemble learning --|g 17.|t Undirected graphical models --|g 18.|t High-dimensional problems: p>> N.
5201 |a "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.
650 0|a Supervised learning (Machine learning)|0 http://id.loc.gov/authorities/subjects/sh94008290
650 0|a Electronic data processing.|0 http://id.loc.gov/authorities/subjects/sh85042288
650 0|a Biology|x Data processing.|0 http://id.loc.gov/authorities/subjects/sh85014206
650 0|a Computational biology.|0 http://id.loc.gov/authorities/subjects/sh2003008355
650 0|a Mathematics|x Data processing.|0 http://id.loc.gov/authorities/subjects/sh85082146
650 0|a Data mining.|0 http://id.loc.gov/authorities/subjects/sh97002073
650 7|a Biology|x Data processing.|2 fast|0 (OCoLC)fst00832406
650 7|a Computational biology.|2 fast|0 (OCoLC)fst00871990
650 7|a Data mining.|2 fast|0 (OCoLC)fst00887946
650 7|a Electronic data processing.|2 fast|0 (OCoLC)fst00906956
650 7|a Mathematics|x Data processing.|2 fast|0 (OCoLC)fst01012179
650 7|a Statistics.|2 fast|0 (OCoLC)fst01132103
650 7|a Supervised learning (Machine learning)|2 fast|0 (OCoLC)fst01139041
655 7|a Statistics.|2 lcgft|0 http://id.loc.gov/authorities/genreForms/gf2014026181
7001 |a Tibshirani, Robert,|0 http://id.loc.gov/authorities/names/n88665311|e author.
7001 |a Friedman, J. H.|q (Jerome H.),|0 http://id.loc.gov/authorities/names/n89648779|e author.
830 0|a Springer series in statistics.|0 http://id.loc.gov/authorities/names/n42023188
907 |a .b41452057
948 |a MARCIVE Comp, 2019.05
948 |a MARCIVE August, 2017
948 |a MARCIVE extract Aug 5, 2017
989 |1 .i82248382|b 1010002103954|d as|g t|m |h 17|x 0|t 0|i 7|j 18|k 140123|n 08-03-2021 19:09|o -|a Q325.75 .H37 2009
994 |a C0|b CLZ
995 |a Loaded with m2btab.ltiac in 2019.05
995 |a Loaded with m2btab.ltiac in 2017.08
998 |e -|f eng|a as