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.
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.
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...
评分douban评论非要给出评价才能发表,这非常难决断 说你好呢,翻译的乱七八糟 说你不好呢,内容实在深刻 说起翻译来,这可是把中文说的比外文还难懂 Jiawei Han的数据挖掘让范明译的污七八糟 结果还让他来翻译这部经典,怀疑他在用google翻译 最后还是忍不住去图书馆复印了原版...
评分https://web.stanford.edu/~hastie/ElemStatLearn/ ==========================================================================================================================================================
值得反复研读。
评分对于machine learning 零基础的人来说,太过生涩了。进阶读物,新手慎入
评分被称为工具书之神,被虐惨了,完全搞不懂
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)
评分讲的和我理解的统计学习不大一样
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