Networks for Learning: Regression and Classification
Free, no sign-up neededSpring 2001English
About this course
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.
Description from MIT OpenCourseWare.
Where to take this course
- Open MIT OpenCourseWare (new tab)MIT OpenCourseWare · Spring 2001