Regularized Least-Squares and Support Vector Machines
We introduce two intances of Tikhonov regularization induced by the square and hinge loss functions.
In particular we derive and compare their computational properties.
Slides for this lecture: PDF
- 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 Machines Advances in Computational Mathematics, 2000.
- Rifkin, R.,. and R.A. Lippert.Notes on Regularized Least-Squares, CBCL Paper #268/AI Technical Report #2007-019, Massachusetts Institute of Technology, Cambridge, MA, May, 2007.
- V. N. Vapnik. The Nature of Statistical Learning Theory. Springer,