The Elements of Statistical Learning pdf epub mobi txt 電子書 下載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.
- 機器學習
- 統計學習
- 數據挖掘
- 統計學
- Statistics
- 數學
- 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.
具體描述
讀後感
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...
評分The methodology used in the books are fancy and attractive, yet in terms of rigorous proofs, sometimes the book skip steps and is difficult to follow. ~ Slightly sophisticated for undergraduate students, but in general is a very nice book.
評分用戶評價
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）
评分被稱為工具書之神，被虐慘瞭，完全搞不懂
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。
评分對於每種方法高屋建瓴的介紹很有啓發性
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。
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