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


The Elements of Statistical Learning

简体网页||繁体网页
T. Hastie 作者
Springer
译者
2003-07-30 出版日期
520 页数
USD 89.95 价格
Hardcover
丛书系列
9780387952840 图书编码

The Elements of Statistical Learning 在线电子书 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   


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

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

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



The Elements of Statistical Learning 在线电子书 用户评价

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内容很多,读起来不是很容易 对于进入这个领域的人来说作为第一本打基础的书很不错

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对于machine learning 零基础的人来说,太过生涩了。进阶读物,新手慎入

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so clear and comprehensive

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最近在看,mark一下

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讲的和我理解的统计学习不大一样

The Elements of Statistical Learning 在线电子书 著者简介

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.


The Elements of Statistical Learning 在线电子书 图书目录


The Elements of Statistical Learning 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

The Elements of Statistical Learning 在线电子书 图书描述

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.

The Elements of Statistical Learning 在线电子书 下载 mobi epub pdf txt 在线电子书下载

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The Elements of Statistical Learning 在线电子书 读后感

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这个简单的书评只是我个人的观点,所以我觉得先了解一下我的背景是有帮助的:本科计算机,数学功底尚可,研究生方向机器学习、数据挖掘相关应用研究。 缺点: 1,阅读此书前,读者需要具备基本的统计学知识,所以书的内容并不“基础”。 2,书中很少涉及到公式推导,细节并不...  

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

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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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评论最下面的部分Version 1是我开始读这本书的时候写的东西,现在加上点基础部分。 对linear algebra, probability 要有非常强的直观认识,对这两个基础学的非常通透。Linear algebra 有几种常用的分解QR, eigendecomposition, SVD,搞清楚它们的作用和几何意义。Bayesian meth...  

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英文原版的官方免费下载链接已经有人在书评中给出了 中文版的译者很可能没有基本的数学知识,而是用Google翻译完成了这部作品。 超平面的Normal equation (法线方程)翻译成了“平面上的标准方程”;而稍有高中髙维几何常识的人都知道,法线是正交与该超平面的方向,而绝不可...  

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