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Material Detail

The Elements of Statistical Learning

The Elements of Statistical Learning

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

Keywords:
Trees, Kernel Smoothing Methods, Additive Models, Model Inference and Averaging, Basis Expansions and Regularization, Random Forests, Undirected Graphical Models, Unsupervised Learning, Boosting and Additive Trees, Neural Networks, and Related Methods, Linear Methods for Regression, Linear Methods for Classification, Ensemble Learning, Supervised Learning, Support Vector Machines and Flexible Discriminants, Prototype Methods and Nearest-Neighbors, Model Assessment and Selection, High-Dimensional Problems
Disciplines:
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More about this material

Material Type:
Open (Access) Textbook
Date Added to MERLOT:
May 20, 2020
Date Modified in MERLOT:
May 20, 2020
Authors:
Submitter:
Rama Komaragiri
Primary Audience:
Graduate School, Professional
Technical Format:
PDF, Website
Mobile Compatibility:
Not specified at this time
Technical Requirements:
PDF reader
Language:
English
Cost Involved:
No
Source Code Available:
Yes
Creative Commons:
Unknown

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