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.
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.
發表於2025-03-26
The Elements of Statistical Learning 2025 pdf epub mobi 電子書 下載
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...
評分個人覺得“機器學習 -- 從入門到精通”可以作為這本書的副標題。 機器學習、數據挖掘或者模式識彆領域有幾本非常流行的教材,比如Duda的模式分類,Bishop的PRML。Duda的書第一版是模式識彆的奠基之作,現在大傢談論得是第二版,因為內容相對簡單,非常流行,但對近20年取得統...
評分上半部看得更仔細些,相對來說收獲也更多。書的前半部對各種迴歸說得很多,曾經僅僅瞭解這些的迴歸方法的大概思路,但是從本書中更能瞭解它們的統計意義、本質,有種豁然開朗的感覺:) 隻是總的來說還是磕磕巴巴的看瞭一遍,還得繼續仔細研讀纔好。希望能有更深刻的領悟,目的...
評分非常難,一點都不element,是本百科全書式的讀物,如果是初學者,不建議讀 很多章節也沒有細節,概述性的東西,能看懂幾章就很不錯瞭 其實每章都可以寫成一本書,都可以做很多篇的論文 全部讀懂非常非常難,倒是作為用到哪個部分作為參考資料查查很不錯
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
1. 一點都不基礎 被虐慘瞭 2. 新手韆萬不要看 3. 得讀好幾遍 = =
評分隻能算斷斷續續地讀瞭其中一些吧
評分隻能算斷斷續續地讀瞭其中一些吧
評分半年攻下!
評分對於machine learning 零基礎的人來說,太過生澀瞭。進階讀物,新手慎入
The Elements of Statistical Learning 2025 pdf epub mobi 電子書 下載