> The Spider Objects

>       MKSVM Multi-Kernel LP-SVM following Weston (PhD-thesis, chapter 6)

>        a=mksvm(kdict,hyperParam)

>        Generates a svm object with given hyperparameters.

>          Hyperparameters (with defaults)
>          kdict={}   		  -- dictionary of kernels (a cell array of kernel objects)
>          C=Inf                -- the soft margin C parameter
>          ridge=1e-13          -- a ridge on the kernel
>          balanced_ridge=0     -- for unbalanced data

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

>        Methods:
>         train, test, get_w

>        Example:
>        use dictionary of 3 kernels
>        [r a]=train(mksvm({kernel,kernel,kernel('rbf',2.5),kernel('rbf',2)}),toy)
>        loss(test(a,toy))

> | Reference : Extensions to the Support Vector Approach, Chapter 6

> | Author : Jason Weston

> | Link : http://www.kyb.tuebingen.mpg.de/bs/people/weston/index.html