圖書標籤: 機器學習 統計學習 數據挖掘 統計學 Statistics 數學 Learning Data-Mining
发表于2025-04-16
The Elements of Statistical Learning pdf epub mobi txt 電子書 下載 2025
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.
講的和我理解的統計學習不大一樣
評分講的和我理解的統計學習不大一樣
評分so clear and comprehensive
評分ESL跟PRML側重很不一樣。前者從frequentist的角度,後者從Bayesian的角度。Machine Learning a Prospective Approach則是二者中閤。 感覺ESL講的東西較PRML直覺性強很多。尤其是bayesian的一堆東西全沒法計算,全是approximation,真用到實戰中頭疼得要死。而ESL上的方法多用bootstraping來近似貝葉斯學派的方法,實現簡單太多。(第8章)
評分多讀幾遍再評論
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...
評分 評分https://web.stanford.edu/~hastie/ElemStatLearn/ ==========================================================================================================================================================
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...
The Elements of Statistical Learning pdf epub mobi txt 電子書 下載 2025