Machine Learning - Support Vector Machines

Klipi teostus: Mirjam Paales 08.05.2012 2851 vaatamist Arvutiteadus


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