> The Spider Objects

>       SVM Support Vector Machine object

>        a=svm(hyperParam)

>        Generates a svm object with given hyperparameters.

>          Hyperparameters (with defaults)
>          child=kernel         -- the kernel is stored as a member called "child"
>          C=Inf                -- the soft margin C parameter
>          ridge=1e-13          -- a ridge on the kernel
>          balanced_ridge=0     -- for unbalanced data
>          nu = 0               -- bernhard's nu svm parameter
>          optimizer='default'  -- other choices={andre,quadprog,svmlight,
>                                                 libsvm,svmtorch}
>          alpha_cutoff=-1;     -- keep alphas with abs(a_i)>alpha_cutoff
>                                  default keeps all alphas, another
>                                  reasonable choice is e.g alpha_cutoff=1e-5 to remove
>                                  zero alphas (i.e non-SVs) to speed up computations.

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

>        Methods:
>         train, test, get_w

>        Example:

>         d=gen(spiral({'m=200','n=2','noise=0.35'}));
>         [r,a]=train(cv(svm({kernel('rbf',1),'optimizer="andre"'})),d)
>         plot(a{1})

> | Reference : A Tutorial on Support Vector Machines for Pattern Recognition

> | Author : Christopher J. C. Burges

> | Link : http://citeseer.ist.psu.edu/burges98tutorial.html