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


The Elements of Statistical Learning

简体网页||繁体网页
T. Hastie
Springer
2003-07-30
520
USD 89.95
Hardcover
9780387952840

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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发表于2025-05-25

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

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

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



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 下载 mobi epub pdf txt 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.


图书目录


The Elements of Statistical Learning pdf epub mobi txt 电子书 下载
想要找书就要到 小哈图书下载中心
立刻按 ctrl+D收藏本页
你会得到大惊喜!!

用户评价

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最近在看,mark一下

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只能算断断续续地读了其中一些吧

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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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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

读后感

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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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The methodology used in the books are fancy and attractive, yet in terms of rigorous proofs, sometimes the book skip steps and is difficult to follow. ~ Slightly sophisticated for undergraduate students, but in general is a very nice book.

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中文翻译版大概是用google翻译翻的,然后排版一下,就出版了。所以中文翻译版中,每个单词翻译是对的,但一句话连起来却怎么也看不懂。最佳阅读方式是,看英文版,个别单词不认识的话,再看中文版对应的那个词。但如果英文版整个句子都不懂的话,那只有去借助baidu/google,并...  

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

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