The Elements of Statistical Learning

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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:Trevor Hastie
出品人:
页数:745
译者:
出版时间:2009-10-1
价格:GBP 62.99
装帧:Hardcover
isbn号码:9780387848570
丛书系列:Springer Series in Statistics
图书标签:
  • 机器学习 
  • 统计学习 
  • Statistics 
  • 统计 
  • 数据挖掘 
  • 统计学 
  • 数学 
  • Data-Mining 
  •  
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During the past decade there has been an explosion in computation and information technology. With it have 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 describes 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 is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (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. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for "wide" data (p bigger than n), including multiple testing and false discovery rates.

具体描述

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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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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上半部看得更仔细些,相对来说收获也更多。书的前半部对各种回归说得很多,曾经仅仅了解这些的回归方法的大概思路,但是从本书中更能了解它们的统计意义、本质,有种豁然开朗的感觉:) 只是总的来说还是磕磕巴巴的看了一遍,还得继续仔细研读才好。希望能有更深刻的领悟,目的...  

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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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上半部看得更仔细些,相对来说收获也更多。书的前半部对各种回归说得很多,曾经仅仅了解这些的回归方法的大概思路,但是从本书中更能了解它们的统计意义、本质,有种豁然开朗的感觉:) 只是总的来说还是磕磕巴巴的看了一遍,还得继续仔细研读才好。希望能有更深刻的领悟,目的...  

用户评价

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大略读了书中2/3的内容。应该说,从统计的角度分析一些方法是对的,但是统计角度未必就是理解许多方法的最佳方式。打算再读两本经典,写点总结。

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快速翻了一下,搞懂了几个之前疑惑的概念,但要细看那些公式真的需要花很多很多时间呢

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年少无知的我啊,竟然在第一次看这本书的时候给了三分并写了这样的评价 “总觉得有些章节编写的前后不合理啊,还有数学和概率功底要求好严格”。 现在再读这本书,觉得写的真是到位,改五分。大神请原谅~

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这本书豆瓣竟然有近400人标记读过,PoliSci的英文书读过人数超过10的都很少。。。

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