The Elements of Statistical Learning Second Edition HARDCOVER - VG

Description: 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. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates. \\ store use: loc - desk/o:dup

Price: 29.75 USD

Location: Detroit, Michigan

End Time: 2024-11-17T21:51:52.000Z

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All returns accepted: ReturnsNotAccepted

Number of Pages: Xxii, 745 Pages

Language: English

Publication Name: Elements of Statistical Learning : Data Mining, Inference, and Prediction

Publisher: Springer New York

Publication Year: 2017

Subject: Probability & Statistics / General, Intelligence (Ai) & Semantics, Databases / Data Mining

Item Height: 1.5 in

Item Weight: 51.2 Oz

Type: Textbook

Subject Area: Computers, Mathematics

Author: Trevor Hastie, Jerome Friedman, Robert Tibshirani, J. H. Friedman

Item Length: 9.4 in

Series: Springer Series in Statistics Ser.

Item Width: 6.5 in

Format: Hardcover