| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000045519728 | |
| 005 | 20210927165726 | |
| 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 ▼g (2017 printing). | |
| 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 |
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| 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 Available | Due Date | 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 Available | Due Date | Make a Reservation | Service |
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Sejong Academic Information Center/Science & Technology/ | Call Number 006.31 H356e2 | Accession No. 151309538 | Availability Available | Due Date | Make a Reservation | Service |
| No. 2 | 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 |
Contents information
Author Introduction
트레버 해이스티(지은이)
통계학과 생물의학 데이터 과학 교수이다. 현대 데이터 분석의 핵심 책인 『통계학으로 배우는 머신러닝 2/e』(에이콘, 2020)의 공저자이다. 일반 가법 모델과 주요 곡선 그리고 R 컴퓨팅 환경에 대한 기여로 잘 알려져 있다. R 컴퓨팅 환경에 대한 공헌을 인정받아 2018년 National Academy of Sciences에 선출됐고, 2019년에는 볼로냐 대학(University of Bologna)으로부터 시길럼 매그넘(Sigillum Magnum)을, 2020년에는 미국 통계 협회로부터 레오 브라이만(Leo Breiman)상을 받았다.
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
