> The Spider Objects

>       RFE recursive feature elimination object

>        A=RFE(C,[H]) returns an rfe object initialized with base algorithm
>        C (usually an svm) and hyperparameters H.

>        RFE selects features via greedy backward selection: it trains a
>        base algorithm, and calls the method get_w of the base algorithm
>        and removes the feature(s) with the smallest absolute value returned
>        from this method. get_w in a linear svm for example returns the
>        weights for each dimension defining the hyperplane. Base algorithms
>        with a method get_w include: one_vs_one, one_vs_rest, lda, stab, svm, svr

>         Hyperparameters, and their defaults

>          feat=[]        -- number of selected features to be selected, if feat=0,
>                            then the minimum number of feature will be computed.
>          feature_group  -- Use this to supply a vector containing a group
>                = []        index for each feature. Features with the same
>                            group index are eliminated together. By default,
>                            the vector 1:n is used, meaning features are
>                            eliminated independently.
>          force=[]       -- one value per feature or feature group. Features/groups
>                            with negative values will be eliminated first (in
>                            increasing order of force value). Features/groups
>                            with positive values will be eliminated last (again,
>                            in increasing force value order). A value of 0 means
>                            that feature elimination order is determined by the
>                            normal method.
>          speed=0        -- rfe only removes a single feature each iteration
>                            if less than (speed) features left, otherwise it
>                            halves the number of features each iteration.
>          output_rank=0  -- whether a ranking is desired, if set to 0 then a
>                            classification is perfomed after feature selection.
>          child=svm      -- The base classifier to be used at each step

>         Model

>         a.rank=[]       -- ranking of features
>         a.child=svm     -- base classifier trained at end of process

>         Methods:
>          train, test

>        Example:
>        d=gen(toy); a=rfe; a.feat=10; a.output_rank=1;[r,a]=train(a,d);
>        a.rank    - lists the chosen features in  order of importance

> | Reference : Gene selection for cancer classification using support vector machines

> | Author : I. Guyon, J. Weston, S. Barnhill, and V. Vapnik

> | Link : http://www.stanford.edu/class/cs374/CLUSTER_Guyon.pdf