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

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.

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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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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...

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douban评论非要给出评价才能发表，这非常难决断说你好呢，翻译的乱七八糟说你不好呢，内容实在深刻说起翻译来，这可是把中文说的比外文还难懂 Jiawei Han的数据挖掘让范明译的污七八糟结果还让他来翻译这部经典，怀疑他在用google翻译最后还是忍不住去图书馆复印了原版...

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