XIII. Support Vector Machines
Given by Konstantin Tretyakov
Brief summary: Recap on algebra and geometry. Maximal margin classifiers. Reformulation as a quadratic programming problem. Primal and dual forms. SVM as an example of a regularized learning problem. Hinge loss as an example of a surrogate loss function.
Slides:(pdf)
Literature:
Cristianini and Shawe-Taylor: An Introduction to Support Vector Machines pages 93 - 112
Schölkopf and Smola: Learning with Kernels pages 189 - 215