The Elements of Statistical Learning pdf epub mobi txt 电子书 下载 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.
- 机器学习
- 统计学习
- 数据挖掘
- 统计学
- Statistics
- 数学
- Learning
- Data-Mining
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.
具体描述
读后感
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...
评分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...
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...
评分个人觉得“机器学习 -- 从入门到精通”可以作为这本书的副标题。 机器学习、数据挖掘或者模式识别领域有几本非常流行的教材，比如Duda的模式分类，Bishop的PRML。Duda的书第一版是模式识别的奠基之作，现在大家谈论得是第二版，因为内容相对简单，非常流行，但对近20年取得统...
评分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...
用户评价
对于machine learning 零基础的人来说，太过生涩了。进阶读物，新手慎入
评分感觉比PRML更清晰
评分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
评分最近在看，mark一下
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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