> The Spider Objects

>       RSC_BURGES reduced set construction - burges method

>        a = rsc_burges(alg,hyper)
>        generates a rsc object, using the burges method

>        hyperparameters:
>        child=svm     algorithm worked on
>        rsv=.5        if rsv e [0,1): #rsv = #sv * rsv
>                      if rsv > 1: #rsv
>        max_it=100    maximum of iterations for gradient descent
>        eps=1e-5      epsilon
>        phase2=yes    perform burges phase2 ?
>        minimizer=1   (1) gradient descent (0) conj. gradient
>        stv=3         number of restarts
>        deriv='der_rbf' use function, which name is supplied as string, as
>                      function to calculate d||w-w*||^2/dz_l
>                      if set to 'num', usee numerical derivative instead

>        model:
>        alpha         new alphas for rs-vectors
>        Xsv           rs vectors

>        stats:
>        w2=0          final value of ||w-w*||^2
>        delta_w=0     total decrease in ||w-w*||^2
>        dw=0          run through delta_w

>        methods:
>        train         constructs a reduced set, returns trained rs-machine
>        test          tests new rs-machine on supplied data

>        example:
>        d=gen(toy2d('2circles','l=100'));
>        [r,a]=train(svm({kernel('rbf',1),'C=10000','alpha_cutoff=1e-2'}),d);
>        [r,a2]=train(rsc_burges(a,'rsv=.9'),d);
>        test(a2,d,loss);

>        author: chris burges
>        reference: simplified sv decision rules, 1996