**The Elements of Statistical Learning**

Over the past decade there has been an explosion in computing and information technology. With it came large amounts of data in a variety of fields such as medicine, biology, finance and marketing. The challenge of understanding this data has led to the development of new tools in the field of statistics and has spawned new areas such as data mining, machine learning and bioinformatics. Many of these tools have a common basis, but are often expressed in different terminology. This book describes the important ideas in these areas in a common conceptual framework. Although the focus 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 (forecasting) to unsupervised learning. The numerous topics include neural networks, support vector machines, classification trees and power supply, the first complete discussion of this topic in any book. This major new edition introduces many topics not covered in the original, including graph models, random forests, ensemble methods, minimum angle regression and lasso path algorithms, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “big” data (p greater than n), including multiple tests and false detection rates.

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

Author(s): Trevor Hastie, Robert Tibshirani, Jerome Friedman

Series: Springer Series in Statistics

Publisher: Springer, Year: 2013

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