The Elements Of Statistical Learning: Data Mining, Inference, And Prediction (Series In Statistics)

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Synopsis
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 should be 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.

Copyright:
2003

Book Details

Book Quality:
Excellent
Book Size:
549 Pages
ISBN-13:
9780387952840
Publisher:
Springer
Date of Addition:
Copyrighted By:
Trevor Hastie, Robert Tibshirani, Jerome Friedman.
Adult content:
No
Language:
English
Has Image Descriptions:
No
Categories:
Nonfiction, Textbooks, Mathematics and Statistics
Submitted By:
Daproim Africa
Proofread By:
Daproim Africa
Usage Restrictions:
This is a copyrighted book.

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