The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
Overview & Synopsis
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.
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Document Information
| Document Title | The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition |
|---|---|
| Author / Organization | Trevor Hastie, Robert Tibshirani, Jerome Friedman |
| Subject & Discipline | Machine Learning, Statistics and SAS Programming, Data Analysis and Data Mining, Probability, Stochastic Process, Queueing Theory, etc. |
| Categories | Machine Learning |
| Publication Year | 2016 |
| ISBN | 0387848576 |
| Resource Verification | ✓ Verified Direct PDF Available |
| Original Host Website |
FreeComputerBooks.com
(freecomputerbooks.com)
|
| File Format | application/pdf (Portable Document Format) |
| Reported File Size | 12991.8 KB (13,303,613 bytes) |
| Language | ENGLISH |
| Catalog ID | DOC-261572 |
| Original Source Link | Visit Source Page on freecomputerbooks.com » |