{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

lec5 (1) - Suggested Reading • Rifkin Everything Old Is...

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
Lecture 5: Support Vector Machines for Classification Ryan Rifkin 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. We introduce Regularized Least Squares regression and classification.
Background image of page 1
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 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 Machines Advances in Computational Mathematics, 2000. • V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, 1995....
View Full Document

{[ snackBarMessage ]}