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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讀後感

評分☆☆☆☆☆

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.

評分☆☆☆☆☆

統計學習的經典教材，數學難度適中，英文難度較低，看瞭其中有監督學習部分，無監督學習部分沒怎麼看，算法比較經典，但是也比較老。

評分☆☆☆☆☆

我導師(stanford博士畢業)非常欣賞這本書，並把它作為我博士資格考試的參考教材之一。感謝 ZHENHUI LI 提供的信息。本書作者已經將第二版的電子書放到網上，大傢可以免費下載。 http://www-stat.stanford.edu/~tibs/ElemStatLearn/ 網上還有一份solution manual, 但是似乎...