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.
發表於2024-06-24
統計學習基礎(第2版)(英文) 2024 pdf epub mobi 電子書 下載
讀 ESL 快半年瞭,也讀瞭差不多1/3,寫個短評記錄一下,等讀完的時候再來改吧。然後簡單對比下基本常見的機器學習教材。 我本科是學物理的,對於統計甚至概率論可以說是一無所知。入門的時候讀的是周誌華老師的《機器學習》,不過並沒有讀完的。一方麵在傢看書效率太低;另一...
評分 評分http://www-stat.stanford.edu/~hastie/local.ftp/Springer/ESLII_print3.pdf
評分 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...
圖書標籤: 機器學習 統計學習 統計學 數據挖掘 數學 統計 數據分析 statistics
算是很“基礎”的一本書,內容覆蓋瞭幾乎10年之前所有與統計學習相關的內容,當然有詳有略。要更近一步還得多多努力纔行
評分A comprehensive book concerning ML. I recommend it to advanced readers equipped with extensive solid mathematical foundation, especially certain core courses of statistics(e.g., multivariate statistical analysis), matrix theory, optimization theory and numerical analysis.
評分統計學習,模式識彆領域,最愛的一本書。推導過程清晰,還有各種感悟和總結,很好。但是講的內容比機器學習少瞭一些,好像是沒有hmm,crf的。
評分統計學習,模式識彆領域,最愛的一本書。推導過程清晰,還有各種感悟和總結,很好。但是講的內容比機器學習少瞭一些,好像是沒有hmm,crf的。
評分A comprehensive book concerning ML. I recommend it to advanced readers equipped with extensive solid mathematical foundation, especially certain core courses of statistics(e.g., multivariate statistical analysis), matrix theory, optimization theory and numerical analysis.
統計學習基礎(第2版)(英文) 2024 pdf epub mobi 電子書 下載