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2024-05-17
The Elements of Statistical Learning 2024 pdf epub mobi 電子書 下載
讀瞭一個月,還在前四章深耕,在此說明一下,網上的 solution,筆記啊,我見到的,隻有一個份做的最詳細,準確度最高,其餘的都是濫竽充數,過程推導亂來,想當然,因為該書的符號有點混亂,所以建議閱讀該書的人把前麵的 Notation 讀清楚,比如書中 X 齣現的有好幾種形式,每...
評分讀 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...
評分英文原版的官方免費下載鏈接已經有人在書評中給齣瞭 中文版的譯者很可能沒有基本的數學知識,而是用Google翻譯完成瞭這部作品。 超平麵的Normal equation (法綫方程)翻譯成瞭“平麵上的標準方程”;而稍有高中髙維幾何常識的人都知道,法綫是正交與該超平麵的方嚮,而絕不可...
評分這個簡單的書評隻是我個人的觀點,所以我覺得先瞭解一下我的背景是有幫助的:本科計算機,數學功底尚可,研究生方嚮機器學習、數據挖掘相關應用研究。 缺點: 1,閱讀此書前,讀者需要具備基本的統計學知識,所以書的內容並不“基礎”。 2,書中很少涉及到公式推導,細節並不...
評分評論最下麵的部分Version 1是我開始讀這本書的時候寫的東西,現在加上點基礎部分。 對linear algebra, probability 要有非常強的直觀認識,對這兩個基礎學的非常通透。Linear algebra 有幾種常用的分解QR, eigendecomposition, SVD,搞清楚它們的作用和幾何意義。Bayesian meth...
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
多讀幾遍再評論
評分對象看書引發我的獵奇心理 看瞭很鬧心
評分ESL跟PRML側重很不一樣。前者從frequentist的角度,後者從Bayesian的角度。Machine Learning a Prospective Approach則是二者中閤。 感覺ESL講的東西較PRML直覺性強很多。尤其是bayesian的一堆東西全沒法計算,全是approximation,真用到實戰中頭疼得要死。而ESL上的方法多用bootstraping來近似貝葉斯學派的方法,實現簡單太多。(第8章)
評分被稱為工具書之神,被虐慘瞭,完全搞不懂
評分瀏覽過,經典之作
The Elements of Statistical Learning 2024 pdf epub mobi 電子書 下載