The Elements of Statistical Learning pdf epub mobi txt 電子書下載2025

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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:

頁數:520

译者:

出版時間:2003-07-30

價格:USD 89.95

裝幀:Hardcover

isbn號碼:9780387952840

叢書系列:

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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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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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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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

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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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