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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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☆☆☆☆☆

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☆☆☆☆☆

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...

評分☆☆☆☆☆

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...