An Introduction to Statistical Learning

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Gareth James is a professor of data sciences and operations 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 an associate professor of statistics and biostatistics at the University of Washington. Her research focuses largely on statistical machine learning in the high-dimensional setting, 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.

出版者:Springer
作者:Gareth James
出品人:
頁數:426
译者:
出版時間:2013-8-12
價格:USD 79.99
裝幀:Hardcover
isbn號碼:9781461471370
叢書系列:Springer Texts in Statistics
圖書標籤:
  • 機器學習 
  • 統計學習 
  • 統計 
  • 數據分析 
  • Statistics 
  • 統計學 
  • machine_learning 
  •  
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

具體描述

讀後感

評分

其实我最大的感触是书中总是说某某内容 “ is beyond the scope of this book” ,真是难为几位作者了。 --------------------------- 高清无码图见相册: https://www.douban.com/photos/photo/2462258822/  

評分

其实我最大的感触是书中总是说某某内容 “ is beyond the scope of this book” ,真是难为几位作者了。 --------------------------- 高清无码图见相册: https://www.douban.com/photos/photo/2462258822/  

評分

1,统计学习的入门书,通俗易懂,号称是ESL的入门版,全书没有太多数学推导,适合学工程的人不适合学统计的人读。2,监督学习占了大部分篇幅,我觉得这本书最好的部分就是模型的讨论都围绕variance和bias的trade-off展开,还有就是对模型的整体性能,以及参数的经验取值都给出...  

評分

这本书读起来不费劲,弱化了数学推导过程,注重思维的直观理解和启发。读起来很畅快,个人感觉第三章线性回归写的很好,即使是很简单的线性模型,作者提出的几个问题和细细的解释这些问题对人很有启发性,逻辑梳理得很好,也易懂。(不过有点可惜的是翻译版本确实不是太好,有些...  

評分

这本书读起来不费劲,弱化了数学推导过程,注重思维的直观理解和启发。读起来很畅快,个人感觉第三章线性回归写的很好,即使是很简单的线性模型,作者提出的几个问题和细细的解释这些问题对人很有启发性,逻辑梳理得很好,也易懂。(不过有点可惜的是翻译版本确实不是太好,有些...  

用戶評價

评分

感覺自己還是學院派,這是截至目前最喜歡的一本機器學習(統計學習)教材,盡管數學原理介紹得也不算深,但總體仍然是重理論、輕代碼、輕應用。

评分

applied regression analysis課的textbook,結果prof就直接拿著stanford learning上兩個作者公開課的 slide直接用瞭。。挺適閤自學的

评分

公開課的教材,沒涉及太多的數學,不錯。https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/courseware/dfece96897994039a17547b575573447/

评分

statistical learning的入門級教材,不需要很多的數學,但涵蓋瞭許多topic,而且每章結尾都有R的實例,不過這本書還是過於基礎,unsupervised learning隻有一章,而且居然跳過瞭neural networks。

评分

相比PRML確實是入門級的,配閤網上的課件和視頻,講得很清楚,主要針對supervised machine learning

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