> The Spider Objects

>            ANORM object

>         Minimize ||\alpha||_norm describing kernel expansion, under
>         separability constraints. [currently only for pattern recognition and
>         norms 0, 1 and 2]

>        A=ANORM(H) returns an anorm object initialized with hyperparameters H.

>        Hyperparameters, and their defaults:
>         norm=1               -- norm to use, i.e. minimize ||\alpha||_norm
>         ridge=1e-12          -- a ridge on the kernel
>         child=kernel         -- the kernel is stored as a member called "child"

>        Model:
>         alpha                -- the weights
>         b0                   -- the threshold
>         Xsv                  -- the Support Vectors

>        Methods:
>         train, test
>        Example:

>         [r,a]=train(anorm('norm=1'),gen(toy));
>         [r,a]=train(anorm('norm=2'),gen(toy));
>         [r,a]=train(anorm({'norm=0',kernel('rbf',1)}),gen(toy));
>        Refernce  : Use of the Zero-Norm With Linear Models and Kernel Methods

> | Author : Jason Weston, André Elisseeff, Bernd Schölkopf, Mike Tipping

> | Link : http://citeseer.ist.psu.edu/531078.html