The Elements of Statistical Learning Data Mining, Inference, and Prediction, Second Edition
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 fiel...
| Main Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
2009, 2009
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| Edition: | 2nd ed. 2009 |
| Series: | Springer Series in Statistics
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Overview of Supervised Learning
- Linear Methods for Regression
- Linear Methods for Classification
- Basis Expansions and Regularization
- Kernel Smoothing 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
- Prototype Methods and Nearest-Neighbors
- Unsupervised Learning
- Random Forests
- Ensemble Learning
- Undirected Graphical Models
- High-Dimensional Problems: p ? N.