The Spider Objects

 
 
 
 
   
  RSC_l1 reduced set selection
   
 


   a = rss_l1(alg,hyper)
   generates a rss object, using the l1 norm
  
   hyperparameters:
   child=svm         algorithm worked on
   reoptimize_b=1    recalculate the threshold b0
   alpha_cutoff=-1   throw away svs with abs(alpha)   optimizer='andre' 
   a.lambda=0        regularizer for selection
                     set to 0 for automatic selection
   penalize_small=1  min \sum (1/alpha_i) beta_i
   dont_use_noisy_pts=0 discards noisy points
   reoptimize=1    
   
   
   model:
   alpha         new alphas for rs-vectors
   Xsv           rs vectors
   b0            the threshold
   w2            final value of ||w*-w^2||^2, set to -1 to calculate
  
   stats:
   w2=0          final value of ||w-w*||^2 
   res=[]        results on a separate test set
   dtst=[]       separate test set  
   test_on=0     iterations to test on
  
   methods:
   train         constructs a reduced set, returns trained rs-machine
   test          tests new rs-machine on supplied data
  
   example:
   d=gen(toy2d('2circles','l=100'));
   [r,a]=train(svm({kernel('rbf',1),'C=10000','alpha_cutoff=1e-2'}),d);
   [r,a2]=train(rss_l1(a,'lambda=1e-2'),d);
   test(a2,d,loss)
  
   author: goekhan bakir, jason weston
   reference: fast binary and multi-output rss, 2004