The Elements of Statistical Learning 2025 pdf epub mobi 电子书

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The Elements of Statistical Learning 2025 pdf epub mobi 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.

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发表于2025-01-22

The Elements of Statistical Learning 2025 pdf epub mobi 电子书

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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...

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https://esl.hohoweiya.xyz/index.html ==========================================================================================================================================================

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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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https://web.stanford.edu/~hastie/ElemStatLearn/ ==========================================================================================================================================================

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The methodology used in the books are fancy and attractive, yet in terms of rigorous proofs, sometimes the book skip steps and is difficult to follow. ~ Slightly sophisticated for undergraduate students, but in general is a very nice book.

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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

The Elements of Statistical Learning 2025 pdf epub mobi 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.

The Elements of Statistical Learning 2025 pdf epub mobi 电子书

想要找书就要到本本书屋

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