9.520-A Networks for Learning: Regression and Classification
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MERLOT II




        

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9.520-A Networks for Learning: Regression and Classification

        

9.520-A Networks for Learning: Regression and Classification

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The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass... More
Material Type: Online Course
Date Added to MERLOT: October 20, 2011
Date Modified in MERLOT: October 20, 2011
Author:
Submitter: Sorel Reisman

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  • Reviewed by members of Editorial board for inclusion in MERLOT.
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Mobile Compatibility: Not specified at this time
Language: English
Cost Involved: no
Source Code Available: unsure
Accessiblity Information Available: unsure
Creative Commons: Creative Commons License
This work is licensed under a Attribution-NonCommercial-ShareAlike 3.0 United States

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