> The Spider Objects

>            MC_SVM object

>        A=MC_SVM(H) returns an mc_svm object initialized with hyperparameters H.

>         Multi-class Support Vector Machine, solving a single
>         optimization problem.

>        Hyperparameters, and their defaults
>         C=Inf                -- the soft margin C parameter
>         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, get_w

>        Example:

>        c1=[-1,1];c2=[1,1];c3=[0,-1];

>        X1= randn(50,2)+repmat(c1,50,1);
>        X2= randn(50,2)+repmat(c2,50,1);
>        X3= randn(50,2)+repmat(c3,50,1);
>          note the class label format!
>        Y1= [ones(50,1),-ones(50,1),-ones(50,1)];
>        Y2= [-ones(50,1),ones(50,1),-ones(50,1)];
>        Y3= [-ones(50,1),-ones(50,1),ones(50,1)];

>        d=data([X1;X2;X3],[Y1;Y2;Y3]);

>        [r,a]=train(one_vs_one(svm(kernel('rbf',2))),d)
>          Test class centers
>        dtest=data([c1;c2;c3]);
>        rtest=test(a,dtest)

> | Reference : Multi-class Support Vector Machines

> | Author : Jason Weston, Chris Watkins

> | Link : http://citeseer.ist.psu.edu/8884.html