> The Spider Objects

>       L0 zero-norm minimization (Weston,Elisseeff dual method)  object

>        A=L0(H) returns a l0 object initialized with hyperparameters H.

>        Minimizes the zero norm of the weight vector subject to
>        separability constraints. Although it is possible to deal with the
>        non-seperable case (see paper), this is not currently implemented.

>         Hyperparameters, and their defaults

>          feat=0         -- number of features to be selected, if feat=0,
>                            then the minimum number of feature will be computed.
>          output_rank=1  -- whether a ranking is desired, if set to 0 then a
>                            classification is perfomed after feature selection.
>          find_min=1     -- stop with the minimum number of features if set to 1
>                            otherwise stop as soon as reach feat nonzero features
>          child=svm      -- Set the classifiers to be used at each step

>         Model

>         a.rank=[]       -- ranking of features
>         a.child=svm     -- base classifier trained at end of process

>         Methods:
>          train, test

>         Example:
>         d=gen(toy); a=l0; 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

>         d=gen(toy); a=l0; a.feat=0; a.output_rank=1;[r,a]=train(a,d);
>         a.rank    - lists the chosen features in  order of importance, using minimum number of features

> | Reference : Use of the zeronorm with linear models and kernel methods

> | Author : J. Weston, A. Elisseeff, B. Schölkopf, and M. Tipping

> | Link : http://www.ai.mit.edu/projects/jmlr//papers/volume3/weston03a/weston03a.pdf