An Introduction to Statistical Learning: with Applications in R 2/e
暫譯: 統計學習導論：R語言應用實例（第二版）
James, Gareth, Witten, Daniela, Hastie, Trevor
商品描述
Preface.- 1 Introduction.- 2 Statistical Learning.- 3 Linear Regression.- 4 Classification.- 5 Resampling Methods.- 6 Linear Model Selection and Regularization.- 7 Moving Beyond Linearity.- 8 Tree-Based Methods.- 9 Support Vector Machines.- 10 Deep Learning.- 11 Survival Analysis and Censored Data.- 12 Unsupervised Learning.- 13 Multiple Testing.- Index.
商品描述(中文翻譯)
前言.- 1 介紹.- 2 統計學習.- 3 線性回歸.- 4 分類.- 5 重抽樣方法.- 6 線性模型選擇與正則化.- 7 超越線性.- 8 基於樹的方法.- 9 支持向量機.- 10 深度學習.- 11 生存分析與截尾數據.- 12 無監督學習.- 13 多重測試.- 索引.
作者簡介
Gareth James is a professor of data sciences and operations, and the E. Morgan Stanley Chair in Business Administration, at the University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area.
Daniela Witten is a professor of statistics and biostatistics, and the Dorothy Gilford Endowed Chair, at the University of Washington. Her research focuses largely on statistical machine learning techniques for the analysis of complex, messy, and large-scale data, with an emphasis on unsupervised learning.
Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. 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.
作者簡介(中文翻譯)
加雷斯·詹姆斯是南加州大學數據科學與運營的教授，以及商業管理的E.摩根·史丹利講座教授。他在統計學習領域發表了大量的方法論研究，特別強調高維度和函數數據。本書的概念框架源自他在該領域的MBA選修課程。
丹妮拉·維滕是華盛頓大學的統計學和生物統計學教授，以及多蘿西·吉爾福德講座教授。她的研究主要集中在統計機器學習技術，用於分析複雜、混亂和大規模數據，特別強調無監督學習。
特雷弗·哈斯提和羅伯特·提布希拉尼是斯坦福大學的統計學教授，也是成功教科書《統計學習的元素》的共同作者。哈斯提和提布希拉尼開發了廣義加法模型，並撰寫了同名的熱門書籍。哈斯提共同開發了R/S-PLUS中的許多統計建模軟體和環境，並發明了主曲線和主表面。提布希拉尼提出了套索（lasso）方法，並是非常成功的《自助法導論》的共同作者。
