The Elements of Statistical Learning Data Mining, Inference, and Prediction

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

数据挖掘 统计 Hastie.pdf (12.59 MB, 需要: 5 个论坛币)

2015-8-7 14:35:59 上传

The Elements of Statistical Learning Data Minin
需要: 5 个论坛币  [购买]

作者:  Trevor Hastie
          Robert Tibshirani
          Jerome Friedman
          Second Edition
出版社: Springer

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 Model Inference 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:

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏12 回帖

关键词：Statistical Data Mining Prediction statistica Inference Robert 出版社 统计

The field of Statistics is constantly challenged by the problems that science
and industry brings to its door. In the early days, these problems often came
from agricultural and industrial experiments and were relatively small in
scope. With the advent of computers and the information age, statistical
problems have exploded both in size and complexity. Challenges in the
areas of data storage, organization and searching have led to the new field
of “data mining”; statistical and computational problems in biology and
medicine have created “bioinformatics.” Vast amounts of data are being
generated in many fields, and the statistician’s job is to make sense of it
all: to extract important patterns and trends, and understand “what the
data says.” We call this learning from data.
The challenges in learning from data have led to a revolution in the statistical sciences. Since computation plays such a key role, it is not surprising
that much of this new development has been done by researchers in other
fields such as computer science and engineering.
The learning problems that we consider can be roughly categorized as
either supervised or unsupervised. In supervised learning, the goal is to predict the value of an outcome measure based on a number of input measures;
in unsupervised learning, there is no outcome measure, and the goal is to
describe the associations and patterns among a set of input measures.

This book is free to download from authors' website.

Here it is:
http://statweb.stanford.edu/~tibs/ElemStatLearn/

Whoever want this book just go there, no need to buy it here.

Crsky7 发表于 2015-8-7 16:25
Whoever want this book just go there, no need to buy it here.

Great， Thank you

Crsky7 发表于 2015-8-7 16:25
Whoever want this book just go there, no need to buy it here.

thx～

很不错的资料，多谢分享。

顶起！！！！！！！