Charlie Frogner

## Description

We derive SVMs from a geometric perspective as well as the regularization perspective. Optimality and duality is introduced to demonstrate how large SVMs can be solved. A comparison is made between SVMs and RLSC.## Slides

Slides for this lecture: PDF## Suggested Reading

- Rifkin.
Everything Old Is New Again: A Fresh Look at Historical Approaches in Machine Learning.MIT Ph.D. Thesis, 2002.- Evgeniou, Pontil and Poggio.
Regularization Networks and Support Vector MachinesAdvances in Computational Mathematics, 2000.- V. N. Vapnik.
The Nature of Statistical Learning Theory.Springer, 1995.