The Elements of Statistical Learning 在线电子书 图书标签: 机器学习 统计学习 数据挖掘 统计学 Statistics 数学 Learning Data-Mining
发表于2024-11-23
The Elements of Statistical Learning 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024
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)
评分感觉比PRML更清晰
评分半年攻下!
评分感觉比PRML更清晰
评分讲的和我理解的统计学习不大一样
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.
统计学习的经典教材,数学难度适中,英文难度较低,看了其中有监督学习部分,无监督学习部分没怎么看,算法比较经典,但是也比较老。
评分评论最下面的部分Version 1是我开始读这本书的时候写的东西,现在加上点基础部分。 对linear algebra, probability 要有非常强的直观认识,对这两个基础学的非常通透。Linear algebra 有几种常用的分解QR, eigendecomposition, SVD,搞清楚它们的作用和几何意义。Bayesian meth...
评分 评分英文原版的官方免费下载链接已经有人在书评中给出了 中文版的译者很可能没有基本的数学知识,而是用Google翻译完成了这部作品。 超平面的Normal equation (法线方程)翻译成了“平面上的标准方程”;而稍有高中髙维几何常识的人都知道,法线是正交与该超平面的方向,而绝不可...
评分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.
The Elements of Statistical Learning 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024