The Elements of Statistical Learning pdf epub mobi txt 電子書 下載 2024


The Elements of Statistical Learning

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T. Hastie
Springer
2003-07-30
520
USD 89.95
Hardcover
9780387952840

圖書標籤: 機器學習  統計學習  數據挖掘  統計學  Statistics  數學  Learning  Data-Mining   


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发表于2024-08-02

The Elements of Statistical Learning epub 下載 mobi 下載 pdf 下載 txt 電子書 下載 2024

The Elements of Statistical Learning epub 下載 mobi 下載 pdf 下載 txt 電子書 下載 2024

The Elements of Statistical Learning pdf epub mobi txt 電子書 下載 2024



圖書描述

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.

The Elements of Statistical Learning 下載 mobi epub pdf txt 電子書

著者簡介

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.


圖書目錄


The Elements of Statistical Learning pdf epub mobi txt 電子書 下載
想要找書就要到 小哈圖書下載中心
立刻按 ctrl+D收藏本頁
你會得到大驚喜!!

用戶評價

評分

ESL跟PRML側重很不一樣。前者從frequentist的角度,後者從Bayesian的角度。Machine Learning a Prospective Approach則是二者中閤。 感覺ESL講的東西較PRML直覺性強很多。尤其是bayesian的一堆東西全沒法計算,全是approximation,真用到實戰中頭疼得要死。而ESL上的方法多用bootstraping來近似貝葉斯學派的方法,實現簡單太多。(第8章)

評分

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

評分

隻能算斷斷續續地讀瞭其中一些吧

評分

對象看書引發我的獵奇心理 看瞭很鬧心

評分

被稱為工具書之神,被虐慘瞭,完全搞不懂

讀後感

評分

中文翻译版大概是用google翻译翻的,然后排版一下,就出版了。所以中文翻译版中,每个单词翻译是对的,但一句话连起来却怎么也看不懂。最佳阅读方式是,看英文版,个别单词不认识的话,再看中文版对应的那个词。但如果英文版整个句子都不懂的话,那只有去借助baidu/google,并...  

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

上半部看得更仔细些,相对来说收获也更多。书的前半部对各种回归说得很多,曾经仅仅了解这些的回归方法的大概思路,但是从本书中更能了解它们的统计意义、本质,有种豁然开朗的感觉:) 只是总的来说还是磕磕巴巴的看了一遍,还得继续仔细研读才好。希望能有更深刻的领悟,目的...  

評分

中文翻译版大概是用google翻译翻的,然后排版一下,就出版了。所以中文翻译版中,每个单词翻译是对的,但一句话连起来却怎么也看不懂。最佳阅读方式是,看英文版,个别单词不认识的话,再看中文版对应的那个词。但如果英文版整个句子都不懂的话,那只有去借助baidu/google,并...  

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...  

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