> The Spider Objects

>            FEAT_SEL object

>        a=feat_sel(feats,rank_alg,classify_alg,hyper)

>        A convenient way of combining a feature ranking algorithm with a classifier
>        for many different numbers of features without retraining the
>        ranking classifier. You should specify a vector of number of
>        features (feats) a ranking algorithm (rank_alg) and a
>        classification algorithm (classify_alg) plus optional hyperparameters.

>          Note: (usually the vector (feats) should be smallest value
>          first as the ranking algorithm will only be trained on the
>          first value, and some algorithms such as
>          l0 and rfe will provide a better ranking this way)

>        Hyperparameters, and their defaults
>         feats=[]               -- features to be tried

>        Model
>         rank_alg, classify_alg -- underlying original algorithms
>         child                  -- combination of algorithms together

>        Methods:
>         train, test

>        Example:
>          perform feature selection with fisher and classification with svm
>          for between 1 and 20 features selected
>        a=feat_sel([1:20],fisher,svm('ridge=0.01'));
>        [tr,a]=train(a,toy);
>        r=test(a,toy)
>        loss(r)