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.
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.
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...
評分非常难,一点都不element,是本百科全书式的读物,如果是初学者,不建议读 很多章节也没有细节,概述性的东西,能看懂几章就很不错了 其实每章都可以写成一本书,都可以做很多篇的论文 全部读懂非常非常难,倒是作为用到哪个部分作为参考资料查查很不错
評分这个简单的书评只是我个人的观点,所以我觉得先了解一下我的背景是有帮助的:本科计算机,数学功底尚可,研究生方向机器学习、数据挖掘相关应用研究。 缺点: 1,阅读此书前,读者需要具备基本的统计学知识,所以书的内容并不“基础”。 2,书中很少涉及到公式推导,细节并不...
評分评论最下面的部分Version 1是我开始读这本书的时候写的东西,现在加上点基础部分。 对linear algebra, probability 要有非常强的直观认识,对这两个基础学的非常通透。Linear algebra 有几种常用的分解QR, eigendecomposition, SVD,搞清楚它们的作用和几何意义。Bayesian meth...
值得反復研讀。
评分講的和我理解的統計學習不大一樣
评分隻能算斷斷續續地讀瞭其中一些吧
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 = =
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