0387848576

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition [Repost]

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) 2nd Edition by Robert Tibshirani
English | 26 Aug. 2009 | ISBN : 0387848576 | 745 Pages | PDF | 12.69 MB
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition (repost)

Trevor Hastie, Robert Tibshirani, Jerome Friedman, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition"
English | 2009 | ISBN: 0387848576 | 767 pages | PDF | 15.2 MB
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Repost)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie
English | PDF(True) | 2009 | 764 Pages | ISBN : 0387848576 | 20.64 MB

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.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Repost)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition By Trevor Hastie, Robert Tibshirani, Jerome Friedman
English | PDF | 2009 | 2009 | 756 Pages | ISBN : 0387848576 | 16 MB

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 Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Repost)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie
English | PDF | 2009 | 764 Pages | ISBN : 0387848576 | 20.64 MB

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.