The Elements of Statistical Learning Data Mining, Inference, and Prediction /
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...
Κύριοι συγγραφείς: | , , |
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Συγγραφή απο Οργανισμό/Αρχή: | |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
New York, NY :
Springer New York : Imprint: Springer,
2009.
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Έκδοση: | Second Edition. |
Σειρά: | Springer Series in Statistics,
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Θέματα: | |
Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.