From: ted.dunning@gmail.com   
      
   rif wrote:   
   > Certainly it depends heavily on the classifier.  I have successfully   
   > trained linear SVMs with orders of magnitude more features than   
   > examples.  For instance,   
   >   
   > Ramaswamy, Tamayo, Rifkin, Mukherjee, Yeang, Angelo, Ladd, Reich,   
   > Latulippe, Mesirov, Poggio, Gerlad, Loda, Lander and   
   > Golub. "Multiclass cancer diagnosis using tumor gene expression   
   > signatures." Proceedings of the National Academy of Science, vol. 98,   
   > no. 26, 18 December 2001.   
   >   
   > Cheers,   
   >   
   > rif   
   >   
      
   Indeed.  If you have a widely separated classes, this can be true.   
      
   And the bounds on performance given by the VC dimension are much more   
   informative than any heuristic such as was mentioned.   
      
   This doesn't defeat the curse of dimensionality, sadly.   
      
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