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.
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.
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...
評分读了一个月,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...
評分读 ESL 快半年了,也读了差不多1/3,写个短评记录一下,等读完的时候再来改吧。然后简单对比下基本常见的机器学习教材。 我本科是学物理的,对于统计甚至概率论可以说是一无所知。入门的时候读的是周志华老师的《机器学习》,不过并没有读完的。一方面在家看书效率太低;另一...
評分对于新手来说,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...
評分读了一个月,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...
感覺比PRML更清晰
评分嗯外國大牛就喜歡給巨難的書起個簡單名字。風格是點到為止和欲言又止,一點都不羅哩羅嗦,有基礎的會熱血沸騰,沒基礎的跟看天書差不多。後幾章習題找不到答案。
评分對象看書引發我的獵奇心理 看瞭很鬧心
评分so clear and comprehensive
评分對象看書引發我的獵奇心理 看瞭很鬧心
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