The Elements of Statistical Learning 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


The Elements of Statistical Learning

简体网页||繁体网页
T. Hastie 作者
Springer
译者
2003-07-30 出版日期
520 页数
USD 89.95 价格
Hardcover
丛书系列
9780387952840 图书编码

The Elements of Statistical Learning 在线电子书 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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The Elements of Statistical Learning 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

The Elements of Statistical Learning 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

The Elements of Statistical Learning 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



The Elements of Statistical Learning 在线电子书 用户评价

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半年攻下!

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值得反复研读。

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最近在看,mark一下

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只能算断断续续地读了其中一些吧

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多读几遍再评论

The Elements of Statistical Learning 在线电子书 著者简介

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.


The Elements of Statistical Learning 在线电子书 图书目录


The Elements of Statistical Learning 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

The Elements of Statistical Learning 在线电子书 图书描述

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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The Elements of Statistical Learning 在线电子书 读后感

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http://www-stat.stanford.edu/~hastie/local.ftp/Springer/ESLII_print3.pdf  

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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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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...  

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对于新手来说,这本书和PRML比起来差太远,新手强烈建议去读PRML,接下来再看这本书。。我就举个最简单的例子吧,这本书的第二章overview of supervised learning和PRML的introduction差太远了。。。。读这本书的overview如果读者没有基础几乎不知所云。。但是PRML通过一个例子...  

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