The Elements of Statistical Learning

The Elements of Statistical Learning pdf epub mobi txt 電子書 下載2025

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 
  •  
想要找書就要到 圖書目錄大全
立刻按 ctrl+D收藏本頁
你會得到大驚喜!!

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.

具體描述

讀後感

評分

評分

https://web.stanford.edu/~hastie/ElemStatLearn/ ==========================================================================================================================================================  

評分

評分

读了一个月,还在前四章深耕,在此说明一下,网上的 solution,笔记啊,我见到的,只有一个份做的最详细,准确度最高,其余的都是滥竽充数,过程推导乱来,想当然,因为该书的符号有点混乱,所以建议阅读该书的人把前面的 Notation 读清楚,比如书中 X 出现的有好几种形式,每...  

評分

有人给我推荐这本书的时候说,有了这本书,就不再需要其他的机器学习教材了。 入手这本书的接下来两个月,我与教材中艰深的统计推断、矩阵、数值算法、凸优化等数学知识展开艰苦的斗争。于是我明白了何谓”不需要其他的机器学习教材“:准确地说,是其他的教材都不需要了;一本...  

用戶評價

评分

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)

评分

講的和我理解的統計學習不大一樣

评分

感覺比PRML更清晰

评分

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)

评分

typo太多瞭,勘誤居然有100多頁。不要買first printing。

本站所有內容均為互聯網搜索引擎提供的公開搜索信息,本站不存儲任何數據與內容,任何內容與數據均與本站無關,如有需要請聯繫相關搜索引擎包括但不限於百度google,bing,sogou

© 2025 qciss.net All Rights Reserved. 小哈圖書下載中心 版权所有