The Elements of Statistical Learning

The Elements of Statistical Learning pdf epub mobi txt 电子书 下载 2025

出版者:Springer
作者:T. Hastie
出品人:
页数:520
译者:
出版时间:2003-07-30
价格:USD 89.95
装帧:Hardcover
isbn号码:9780387952840
丛书系列:
图书标签:
  • 机器学习
  • 统计学习
  • 数据挖掘
  • 统计学
  • Statistics
  • 数学
  • Learning
  • Data-Mining
  • statistical learning
  • machine learning
  • data science
  • statistics
  • ross
  • berg
  • technology
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具体描述

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.

作者简介

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.

目录信息

读后感

评分

https://esl.hohoweiya.xyz/index.html ==========================================================================================================================================================  

评分

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...  

评分

对于新手来说,这本书和PRML比起来差太远,新手强烈建议去读PRML,接下来再看这本书。。我就举个最简单的例子吧,这本书的第二章overview of supervised learning和PRML的introduction差太远了。。。。读这本书的overview如果读者没有基础几乎不知所云。。但是PRML通过一个例子...  

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http://www-stat.stanford.edu/~hastie/local.ftp/Springer/ESLII_print3.pdf  

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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...  

用户评价

评分

ESL跟PRML侧重很不一样。前者从frequentist的角度,后者从Bayesian的角度。Machine Learning a Prospective Approach则是二者中合。 感觉ESL讲的东西较PRML直觉性强很多。尤其是bayesian的一堆东西全没法计算,全是approximation,真用到实战中头疼得要死。而ESL上的方法多用bootstraping来近似贝叶斯学派的方法,实现简单太多。(第8章)

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Amazon上面能够看到第二版的信息了,但是不知道相应的电子书哪年才能等到。去年老师总是对我说,这本书很难很难...就决定拿它来祭旗吧

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1. 一点都不基础 被虐惨了 2. 新手千万不要看 3. 得读好几遍 = =

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ESL跟PRML侧重很不一样。前者从frequentist的角度,后者从Bayesian的角度。Machine Learning a Prospective Approach则是二者中合。 感觉ESL讲的东西较PRML直觉性强很多。尤其是bayesian的一堆东西全没法计算,全是approximation,真用到实战中头疼得要死。而ESL上的方法多用bootstraping来近似贝叶斯学派的方法,实现简单太多。(第8章)

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so clear and comprehensive

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