> The Spider Objects

>       RSC_MP reduced set selection

>        a = rss_mp(alg,hyper)
>        generates a rss object, using the matching pursuit selection method

>        hyperparameters:
>        child=svm         algorithm worked on
>        max_loops=1e5     maximum number of basis functions
>        tolerance=1e-5    tolerated loss in ||w-w*||^2
>        backfit=1         backfit on every nth iteration
>        backfit_at_start=100  always backfit for first e.g. 100 iterations
>        dont_revisit=1    dont return to old basis function optimization, always get new one
>        reoptimize_b=1    recalculate the threshold b0
>        alpha_cutoff=0    throw away svs with abs(alpha)   bal_w=0           treat multiple w's as equal by normalizing by length
>        optimizer='iterative' iterative update of matrix inverse

>        model:
>        alpha         new alphas for rs-vectors
>        Xsv           rs vectors
>        b0            the threshold

>        stats:
>        w2=0          final value of ||w-w*||^2
>        res=[]        results on a separate test set
>        dtst=[]       separate test set
>        test_on=0     iterations to test on

>        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(rss_mp(a,'tolerance=1e-2'),d);
>        test(a2,d,loss)

>        author: goekhan bakir, jason weston
>        reference: fast binary and multi-output rss, 2004