The Elements of Statistical Learning

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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.

出版者:Springer
作者:T. Hastie
出品人:
页数:520
译者:
出版时间:2003-07-30
价格:USD 89.95
装帧:Hardcover
isbn号码:9780387952840
丛书系列:
图书标签:
  • 机器学习 
  • 统计学习 
  • 数据挖掘 
  • 统计学 
  • Statistics 
  • 数学 
  • Learning 
  • Data-Mining 
  •  
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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.

具体描述

读后感

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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...  

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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...  

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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。  

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个人觉得“机器学习 -- 从入门到精通”可以作为这本书的副标题。 机器学习、数据挖掘或者模式识别领域有几本非常流行的教材,比如Duda的模式分类,Bishop的PRML。Duda的书第一版是模式识别的奠基之作,现在大家谈论得是第二版,因为内容相对简单,非常流行,但对近20年取得统...  

评分

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...  

用户评价

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值得反复研读。

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so clear and comprehensive

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讲的和我理解的统计学习不大一样

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typo太多了,勘误居然有100多页。不要买first printing。

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最近在看,mark一下

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