lecture05 - Suggested Reading Rifkin. Everything Old Is New...

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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.
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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....
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This note was uploaded on 11/11/2011 for the course BIO 9.07 taught by Professor Ruthrosenholtz during the Spring '04 term at MIT.

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