Elements Of Statistical Learning: Data Mining, Inference, And Prediction, Second Edition by Jerome Friedman and Trevor Hastie and Robert Tibshirani

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 colour 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 factorisation, and spectral clustering.

Publisher:	Springer
Year:	2016
Binding:	Hardback
ISBN:	9780387848570
Condition:	Good
Description:	No DJ As Issued; Heavy. Cover rubbed, sunned and stained, text very good condition.

Note: This image is from a stock photo and may differ from the actual cover.

SKU: 1598342
Location: Math