Key SVM References

from Helen Zhang, 5/14/03



Wahba's web page:

http://www.stat.wisc.edu/~wahba/trindex.html



Good General intuition:

Lin, Y., Wahba, G., Zhang, H., and Lee, Y. " Statistical Properties and Adaptive Tuning of Support Vector Machines. " TR 1022, September 2000.
     Has appeared in Machine Learning, 48, 115-136, 2002. 



GCV style tuning:

Wahba, G., Lin, Y. and Zhang, H. " Generalized Approximate Cross Validation for Support Vector Machines, or, Another Way to Look at
     Margin-Like Quantities " TR 1006, April 1999. Expanded version of TR1006 posted here February 1999. (With revisions) in `Advances in Large Margin
     Classifiers, Smola, Bartlett, Scholkopf and Schurmans, eds., MIT Press (2000), 297-309. 

Wahba, G., Lin, Y., Lee, Y. and Zhang, H. " Optimal Properties and Adaptive Tuning of Standard and Nonstandard Support Vector Machines " TR
     1045, October 2001. In Nonlinear Estimation and Classification, Denison, Hansen, Holmes, Mallick and Yu, eds, Springer, 125-143, 2002. (Supercedes TR
     1039). 

T. Joachims. Estimating the generalization performance of a SVM efficiently. In Proceedings of the International Conference on
     Machine Learning, San Francisco, 2000. Morgan Kaufman.



Multi-Category:

Lee, Y., Lin, Y. and Wahba, G. " Multicategory Support Vector Machines, Theory, and Application to the Classification of Microarray Data and
     Satellite Radiance Data " TR 1064, September 2002, submitted to JASA.



Feature Selection:

P. S. Bradley and O. L. Mangasarian. Feature selection via concave minimization and support vector machines. In J. Shavlik, editor,
     Machine Learning Proceedings of the Fifteenth International Conference(ICML '98), pages 82-90, San Francisco, California,
     1998. Morgan Kaufmann. 



Kernel Machines Web Page:

http://www.kernel-machines.org/

Tutorials:  

C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining, 2(2),
     1998. 


Books:

Good (?) Starting point:

Nello Cristianini and John Shawe-Taylor. An Introduction to Support Vector Machines. Cambridge University Press,
     Cambridge, UK, 2000.  



Good Complete Treatment:

Bernhard Schölkopf and Alex Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002. 





Kernel Methods:   Implicit & Explicit Embedding