520/600.774 Kernel Machine Learning
Statistical learning theory and kernel based pattern
recognition. Topics include kernel methods, large margin classifiers,
support vector machines, regularization networks, gaussian processes,
sparse approximation, and applications in vision and speech.
Assignments include a class project and
presentation of original work.
Instructor: Prof. Gert
Cauwenberghs, Barton 209/400B,
Time and location: Th 1:30-4, Barton 225
B. Schoelkopf and A.J. Smola, Learning with
Kernels, Cambridge MA: MIT Press, 2002.
V. Vapnik, The Nature of Statistical Learning Theory, 2nd ed.,
B. Schoelkopf, C.J.C. Burges and A.J. Smola, eds., Advances in
Kernel Methods, Cambridge MA: MIT Press, 1999.
Kernel machines repository,
Networks for Learning: Regression and Classification, taught by Alessandro
Verri and Tomaso
Poggio at MIT.
Incremental and decremental SVM learning
December 16, 2002