The Elements of Statistical Learning

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.

The Elements of Statistical Learning

The Elements of Statistical Learning

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 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 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. 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.

More Books:

The Elements of Statistical Learning
Language: en
Pages: 536
Authors: Trevor Hastie, Robert Tibshirani, Jerome Friedman
Categories: Mathematics
Type: BOOK - Published: 2013-11-11 - Publisher: Springer Science & Business Media

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
The Elements of Statistical Learning
Language: en
Pages: 745
Authors: Trevor Hastie, Robert Tibshirani, Jerome H. Friedman
Categories: Biology
Type: BOOK - Published: 2009 - Publisher:

Books about The Elements of Statistical Learning
The Elements of Statistical Learning
Language: en
Pages: 546
Authors: R. Tibshirani, J. Friedman
Categories: Artificial intelligence
Type: BOOK - Published: 2001 - Publisher:

During the past decade there has been an explosion in computation and information technology.; With it has come a vast amount 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
An Introduction to Statistical Learning
Language: en
Pages: 426
Authors: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Categories: Mathematics
Type: BOOK - Published: 2013-06-24 - Publisher: Springer Science & Business Media

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents
The Elements of Statistical Learning
Language: en
Pages: 422
Authors: Keith Glover
Categories: Mathematics
Type: BOOK - Published: 2016-12-05 - Publisher: Createspace Independent Publishing Platform

The Elements of Statistical Learning 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 factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including

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