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Statistical Learning Theory and Applications

Massachusetts Institute of TechnologyUnited States9.520
Free, no sign-up neededSpring 2006English
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Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection.

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