统计学习基础(第2版)(英文)

书名：统计学习基础(第2版)(英文)
作者：TrevorHastie/RobertTibsiranl/JeromeFriedman
译者：
ISBN：9787510084508
出版社：世界图书出版公司
出版时间：2015-1-1
格式：epub/mobi/azw3/pdf
页数：745
豆瓣评分： 9.5

书籍简介：

This book is our attempt to bring together many of the important new ideas in learning, and explain them in a statistical framework. While some mathematical details are needed, we emphasize the methods and their conceptual underpinnings rather than their theoretical properties. As a result, we hope that this book will appeal not just to statisticians but also to researchers and practitioners in a wide variety of fields.

作者简介：

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.

书友短评：

@ 流萤天下 感觉翻译为《统计学习精要》好一些，私以为这是目前统计学习领域首屈一指的参考书 @ 氥氲 书名翻译有误。应该译为《统计学习精要》比较好，数学基础不好的可以对照着《统计学习导论》学习，从事机器学习理论研究的应该要看看《统计学习理论》这本著作。总的来说，如果时间充裕的话，还是必须要高屋建瓴，看一些深刻的书籍的。只有打好严谨扎实的基础，才能跟上机器学习领域的发展呐== @ Autoz 本书叫基础，其实是统计学习精要，数学基础比较差的连符号都费劲，虽然满满的文字解析，有另外一本《统计学习导论》,书是 好书就得品着读 @ 琴弦 楼下有几位兄台对“基础”的要求未免太苛刻了，这是面向研究生的书籍，应该用评价GTM的标准来衡量它啊。而且本书的门槛是本科那些知识学扎实就可以读了，做学问来说这难道还不够基础么？ @ 智障儿童欢乐多 ESL：ML专著。可翻 @ Roy 太牛批了 有些算法虽然已经了解过 但是从没想过居然会有那么深的理论基础 每次阅读都有一种 哇！这个居然这么精妙 的感觉 @ lllylll 讨论班一直在讲这本书，难度对我来说挺高的，但是读透了就会有种醍醐灌顶的感觉，机器学习的必读书目之一吧。 @ 蘧公孙 本书面向的读者对象为精通概率统计的人，即你差不多是个统计学博士就可以了，不然很多结论直接就来，也不推导。本书重概率统计直觉，我觉得此书很尴尬，厉害的人觉得就是个提纲，水平差的人又觉得太跳跃。其实不如直接看相关算法论文，这本书的三位作者真不会写书 @ 金融民工 名气很大，内容很散，不如直接读论文

Preface to the Second Edition
Preface to the First Edition
1 Introduction
2 Overview of Supervised Learning
3 Linear Methods for Regression
4 Linear Methods for Classification
5 Basis Expansions and Regularization
6 Kernel Smoothing Methods
7 Model Assessment and Selection
8 Modellnference and Averaging
9 Additive Models, Trees, and Related Methods
10 Boosting and Additive Trees
11 Neural Networks
12 Support Vector Machines and Flexible Discriminants
13 Prototype Methods and Nearest-Neighbors
14 Unsupervised Learning
15 Random Forests
16 Ensemble Learning
17 Undirected Graphical Models
18 High-Dimensional Problems: p≥N
References
Author Index
Index
· · · · · ·

　　Springer Series in Statistics 影印版(共11册)，这套丛书还有《基于回归视野的统计学习》《线性模型》《统计学习基础》《统计学中的渐近性》《高维数据统计学》等。

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