Support Vector Machines
Given by Sven Laur
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 surrogate loss functions.
Slides: (pdf)
Video: UTTV(2016) UTTV (2015) UTTV(2014)
Literature:
- Cristianini and Shawe-Taylor: An Introduction to Support Vector Machines pages 93 - 112
- Schölkopf and Smola: Learning with Kernels pages 189 - 215