SPIDER The Spider Objects

Feature scaling/selection via SVMs and r^2/w^2 bound.

    A=R2W2_SEL(H) returns an r2w2_sel object with hyperparameters H. 
    Hyperparameters, and their defaults
     feat=[]              -- number of features, (default means
                             use scaling factors instead)
     output_rank=0        -- output rank (if 0, output predicted labels)
     slacks=0;            -- if slacks=1,optimize ridge as well, 
                             if this is a vector it specifies
                             which examples share slacks (e.g 3 examples,
                             slacks=[1 1 2] means first two examples
                             have a shared slack variable)
     scales=1;            -- if scales=1, find scaling factors, if 0
                             do not, if scale is a vector, specifies
                             sharing of scaling factors as in slacks parameter
     max_iter=100         -- maximum number of gradient steps 
     steep_ascent=0       -- use steepest grad ascent (can be faster when
                             there are many features) 
     use_var2=1           -- use var^2w^2 estimate instead of r^2w^2
     ker='weighted_linear'-- type of kernel, other choices='weighted_poly'
                             or 'weighted_rbf' 
     kerparam             -- associated param, e.g degree of poly
     optimizer='default'  -- choices={default,andre,quadprog,svmlight}
     rank                 -- ranking of the features
     child                -- decision rule stored in child member (svm)
     sigma                -- value of all scaling and slacking factors
    d=gen(toy); a=r2w2_sel; a.feat=20; a.output_rank=1;[r,a]=train(a,d);
    a.rank    - lists the chosen features in  order of importance, using 20 features

Reference : Feature selection for SVMs
Author : J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik
Link : http://www.kyb.tuebingen.mpg.de/bs/people/weston/FEATURE_SEL.PS