The Elements of Statistical Learning pdf epub mobi txt 電子書 下載2025
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: 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. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
- 機器學習
- 統計學習
- 數據挖掘
- 統計學
- Statistics
- 數學
- Learning
- Data-Mining
During the past decade there has been an explosion in computation and information technology. With it has 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 descibes 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. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learing (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful <EM>An Introduction to the Bootstrap</EM>. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.
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讀後感
统计学习的经典教材，数学难度适中，英文难度较低，看了其中有监督学习部分，无监督学习部分没怎么看，算法比较经典，但是也比较老。
評分读 ESL 快半年了，也读了差不多1/3，写个短评记录一下，等读完的时候再来改吧。然后简单对比下基本常见的机器学习教材。 我本科是学物理的，对于统计甚至概率论可以说是一无所知。入门的时候读的是周志华老师的《机器学习》，不过并没有读完的。一方面在家看书效率太低；另一...
評分读了一个月，还在前四章深耕，在此说明一下，网上的 solution，笔记啊，我见到的，只有一个份做的最详细，准确度最高，其余的都是滥竽充数，过程推导乱来，想当然，因为该书的符号有点混乱，所以建议阅读该书的人把前面的 Notation 读清楚，比如书中 X 出现的有好几种形式，每...
評分我导师(stanford博士毕业)非常欣赏这本书，并把它作为我博士资格考试的参考教材之一。 感谢 ZHENHUI LI 提供的信息。本书作者已经将第二版的电子书放到网上，大家可以免费下载。 http://www-stat.stanford.edu/~tibs/ElemStatLearn/ 网上还有一份solution manual, 但是似乎...
評分英文原版的官方免费下载链接已经有人在书评中给出了 中文版的译者很可能没有基本的数学知识，而是用Google翻译完成了这部作品。 超平面的Normal equation （法线方程）翻译成了“平面上的标准方程”；而稍有高中髙维几何常识的人都知道，法线是正交与该超平面的方向，而绝不可...
用戶評價
對象看書引發我的獵奇心理 看瞭很鬧心
评分瀏覽過，經典之作
评分半年攻下！
评分1. 一點都不基礎 被虐慘瞭 2. 新手韆萬不要看 3. 得讀好幾遍 = =
评分內容很多，讀起來不是很容易 對於進入這個領域的人來說作為第一本打基礎的書很不錯
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