The Elements of Statistical Learning Data MiningInference Prediction 2nd Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman ISBN 0387848576 9780387848570
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Product details:
ISBN 10: 0387848576
ISBN 13: 978-0387848570
Author: Trevor Hastie, Robert Tibshirani, Jerome Friedman
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.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd Table of contents:
1. Introduction
2. Overview of Supervised Learning
3. Linear Methods for Regression
4. Linear Methods for Classification
5. Basis Expansions and Regulariza- tion
6. Kernel Smoothing Methods
7. Model Assessment and Selection
8. Model Inference and Averaging
9. Additive Models, Trees, and Related Methods
10. Boosting and Additive Trees
11. Neural Networks
12. Support Vector Machines and Flexible Discriminants
13. Prototype Nearest-Neighbors Methods and
14. Unsupervised Learning
15. Random Forests
16. Ensemble Learning
17. Undirected Graphical Models
18. High-Dimensional Problems
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