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2025-04-27
The Elements of Statistical Learning 2025 pdf epub mobi 電子書 下載
douban評論非要給齣評價纔能發錶,這非常難決斷 說你好呢,翻譯的亂七八糟 說你不好呢,內容實在深刻 說起翻譯來,這可是把中文說的比外文還難懂 Jiawei Han的數據挖掘讓範明譯的汙七八糟 結果還讓他來翻譯這部經典,懷疑他在用google翻譯 最後還是忍不住去圖書館復印瞭原版...
評分 評分對於新手來說,這本書和PRML比起來差太遠,新手強烈建議去讀PRML,接下來再看這本書。。我就舉個最簡單的例子吧,這本書的第二章overview of supervised learning和PRML的introduction差太遠瞭。。。。讀這本書的overview如果讀者沒有基礎幾乎不知所雲。。但是PRML通過一個例子...
評分https://web.stanford.edu/~hastie/ElemStatLearn/ ==========================================================================================================================================================
評分有人給我推薦這本書的時候說,有瞭這本書,就不再需要其他的機器學習教材瞭。 入手這本書的接下來兩個月,我與教材中艱深的統計推斷、矩陣、數值算法、凸優化等數學知識展開艱苦的鬥爭。於是我明白瞭何謂”不需要其他的機器學習教材“:準確地說,是其他的教材都不需要瞭;一本...
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
對象看書引發我的獵奇心理 看瞭很鬧心
評分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typo太多瞭,勘誤居然有100多頁。不要買first printing。
評分嗯外國大牛就喜歡給巨難的書起個簡單名字。風格是點到為止和欲言又止,一點都不羅哩羅嗦,有基礎的會熱血沸騰,沒基礎的跟看天書差不多。後幾章習題找不到答案。
The Elements of Statistical Learning 2025 pdf epub mobi 電子書 下載