520/600.774 Kernel Machine Learning

Johns Hopkins University
Spring 2003

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, gert@jhu.edu

Time and location: Th 1:30-4, Barton 225

Schedule

References

  • B. Schoelkopf and A.J. Smola, Learning with Kernels, Cambridge MA: MIT Press, 2002.
  • V. Vapnik, The Nature of Statistical Learning Theory, 2nd ed., Springer, 2000.
  • B. Schoelkopf, C.J.C. Burges and A.J. Smola, eds., Advances in Kernel Methods, Cambridge MA: MIT Press, 1999.
  • Kernel machines repository, http://www.kernel-machines.org/
  • 9.520: Networks for Learning: Regression and Classification, taught by Alessandro Verri and Tomaso Poggio at MIT.
  • Incremental and decremental SVM learning Matlab tools

    Gert Cauwenberghs
    December 16, 2002