共享创造价值
为免费资源而生

The Elements of Statistical Learning

The Elements of Statistical Learning

内容简介

During the past decade there has been an explosion in computation and information technology. With it have 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 describes 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 is a valuable resource for statisticians and anyone interested in data mining in science or industry.

The book’s coverage is broad, from supervised learning (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.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates.


获取:

The Elements of Statistical Learning Data Mining, Inference, and Prediction (2nd edition) (12print 2017) by Trevor Hastie, Robert Tibshirani, Jerome Friedman.pdf

下载说明:

在下载页面点击“普通下载”即可,可能会弹出第三方广告页面,请忽略。 详细下载教程请点这里:本站下载教程

电子书格式转换及格式科普请点这里:电子书格式

赞(1)
未经允许不得转载:淇淇有料 » The Elements of Statistical Learning
分享到: 更多 (0)