| 000 | 01096camuu2200313 a 4500 | |
| 001 | 000045519728 | |
| 005 | 20121012221611 | |
| 008 | 951108s2009 nyua b 001 0 eng d | |
| 010 | ▼a 2008941148 | |
| 020 | ▼a 0387848576 | |
| 020 | ▼a 9780387848570 | |
| 040 | ▼a DLC ▼c DLC ▼d 211009 ▼d 244002 ▼d 211009 | |
| 050 | 1 4 | ▼a Q325.75 ▼b H37 2009 |
| 082 | 0 4 | ▼a 006.3/1 ▼2 23 |
| 084 | ▼a 006.31 ▼2 DDCK | |
| 090 | ▼a 006.31 ▼b H356e2 | |
| 100 | 1 | ▼a Hastie, Trevor. |
| 245 | 1 4 | ▼a The elements of statistical learning : ▼b data Mining, inference, and prediction / ▼c Trevor Hastie, Robert Tibshirani, Jerome Friedman. |
| 250 | ▼a 2nd ed. | |
| 260 | ▼a New York : ▼b Springer, ▼c 2009. | |
| 300 | ▼a xxii, 745 p. : ▼b ill. ; ▼c 22 cm. | |
| 490 | 1 | ▼a Springer series in statistics |
| 504 | ▼a Includes bibliographical references (p. [699]-727) and index. | |
| 650 | 0 | ▼a Supervised learning (Machine learning) |
| 700 | 1 | ▼a Tibshirani, Robert. |
| 700 | 1 | ▼a Friedman, J. H. ▼q (Jerome H.) |
| 830 | 0 | ▼a Springer series in statistics. |
| 945 | ▼a KINS |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
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| No. 1 | Location Main Library/Western Books/ | Call Number 006.31 H356e2 | Accession No. 111536097 | Availability Available | Due Date | Make a Reservation | Service |
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| No. 5 | Location Sejong Academic Information Center/Course Reserves/ | Call Number 응용통계학과 006.31 H356e2 | Accession No. 151312573 | Availability Loan can not(reference room) | Due Date | Make a Reservation | Service |
| No. 6 | Location Sejong Academic Information Center/Science & Technology/ | Call Number 006.31 H356e2 | Accession No. 151309538 | Availability Available | Due Date | Make a Reservation | Service |
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Main Library/Western Books/ | Call Number 006.31 H356e2 | Accession No. 111536097 | Availability Available | Due Date | Make a Reservation | Service |
| No. 2 | Location Main Library/Western Books/ | Call Number 006.31 H356e2 | Accession No. 111789110 | Availability In loan | Due Date 2021-04-26 | Make a Reservation | Service |
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Science & Engineering Library/Sci-Info(Stacks2)/ | Call Number 006.31 H356e2 | Accession No. 121221460 | Availability Available | Due Date | Make a Reservation | Service |
| No. 2 | Location Science & Engineering Library/Sci-Info(Stacks2)/ | Call Number 006.31 H356e2 | Accession No. 121239021 | Availability In loan | Due Date 2021-07-05 | Make a Reservation | Service |
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Sejong Academic Information Center/Course Reserves/ | Call Number 응용통계학과 006.31 H356e2 | Accession No. 151312573 | Availability Loan can not(reference room) | Due Date | Make a Reservation | Service |
| No. 2 | Location Sejong Academic Information Center/Science & Technology/ | Call Number 006.31 H356e2 | Accession No. 151309538 | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Author Introduction
트레버 헤이스티(지은이)
통계학 및 생물의학 데이터 과학 교수다. 현대 데이터 분석 분야의 필독서로 꼽히는 『Elements of Statistical Learning, Second Edition』(Springer, 2016)의 공동 저자며, 일반 가첨(additive) 모델과 주요 곡선, R 컴퓨팅 환경에 대한 공로로 잘 알려져 있다. 2014년 통계적 혁신 부문의 '엠마누엘(Emmanuel)과 캐롤 파르젠(Carol Parzen) 상'을 수상했다.
로버트 팁시라니(지은이)
스탠퍼드대학교 통계학과 교수이며, 이 분야의 저명한 연구자다. 일반화 가법 모델을 개발했으며 이 제목으로 인기 있는 책을 저술했다. 라쏘를 제안했으며 이 분야의 매우 유명한 책인 『An introduction to the Bootstrap』(Chapman and Hall/CRC, 1993)의 공동 저자다.
Table of Contents
Introduction 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