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Springer Series in Statistics: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Hardback)
"During the past decade, there has been an explosion in computation and information technology. With it has 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." — From the back cover
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LIKE NEW: This book is in excellent, like-new condition. It was printed on acid-free paper.
Continued from the back cover: “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 book is part of the Springer Series in Statistics and contains 200 full-color illustrations. Each chapter contains exercises and bibliographical notes.
- Overview of Supervised Learning
- Linear Methods for Regression
- Linear Methods for Classification
- Basis Expansions and Regularization
- Kernel Methods
- Model Assessment and Selection
- Model Inference and Averaging
- Additive Models, Trees, and Related Methods
- Boosting and Additive Trees
- Neural Networks
- Support Vector Machines and Flexible Discriminants
- Unsupervised Learning
The book concludes with References, Author Index, and Index.
- Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome.
- Publish Date:
- 2001 (Corrected printing, 2003)
- Weight (pounds):
- Dimensions (W”xL”xH”):
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