The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) ISBN-13: 8904252597924

ISBN-13: 8904252597924

Binding: Hardcover

Author: Hastie, Trevor

Details: 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 books 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. There is also a chapter on methods for “wide data (p bigger than n), including multiple testing and false discovery rates.

Release Date: 21-04-2017

Package Dimensions: 9.5 x 6.5 x 1.3 inches

Languages: English

Part Number: 25645136

Edition: 2nd

Features:

• Language Published: English
• Binding: Hardcover
• Comes in Good condition

Number Of Pages: 767