| The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) |  | Authors: Trevor Hastie, Robert Tibshirani, Jerome Friedman
Publisher: Springer
Category: Book
List Price: $89.95
Buy New: $61.55
as of 5/26/2012 13:00 EDT details
You Save: $28.40 (32%)
New (36) Used (34) from $54.00
Seller: sweethomeliquid2
Sales Rank: 20,076
Languages: English (Unknown), English (Original Language), English (Published)
Media: Hardcover
Edition: 2nd ed. 2009. Corr. 3rd printing 5th Printing.
Pages: 768
Number Of Items: 1
Shipping Weight (lbs): 3.2
Dimensions (in): 9.2 x 6.2 x 1.5
ISBN: 0387848576
EAN: 9780387848570
ASIN: 0387848576
Publication Date: February 9, 2009
Availability: Usually ships in 1-2 business days
| |
| Also Available In:
|
| Similar Items:
| |
| Editorial Reviews:
Product Description
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 & 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.
|
| |
|
|
|
CERTAIN CONTENT THAT APPEARS ON THIS SITE COMES FROM AMAZON SERVICES LLC. THIS CONTENT IS PROVIDED ‘AS IS’ AND IS SUBJECT TO CHANGE OR REMOVAL AT ANY TIME. Powered by Associate-O-Matic
| |
