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

This book descibes the important ideas of data mining, machine learning, and bioinformatics in a common conceptual framework. Topics include neural networks, support vector machines, classification trees and boosting.

**Tag(s):**
Data Mining
Machine Learning
Statistics

**Publication date**: 01 Dec 2015

**ISBN-10**:
0387848576

**ISBN-13**:
978-038784857

**Paperback**:
745 pages

**Views**: 5,240

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

This book descibes the important ideas of data mining, machine learning, and bioinformatics in a common conceptual framework. Topics include neural networks, support vector machines, classification trees and boosting.

From the Back Cover:

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 and 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.

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 and 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.

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About The Author(s)

Dr. Friedman is one of the world's leading researchers in statistics and data mining. He has been a Professor of Statistics at Stanford University for over 20 years and has published on a wide range of data mining topics including nearest neighbor classification, logistical regressions, and high-dimensional data analysis. His primary research interest is in the area of machine learning.

Trevor Hastie is The John A. Overdeck Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University.

Robert Tibshirani is a Professor of Health Research and Policy, and Statistics at Stanford University.

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