> The Spider Objects

>            GROUP object

>         A=GROUP(I,[G]) returns a group object initialized with
>         a cell array of algorithms I and an optional grouping type G.

>         This is used to collect a group of algorithms.

>         Examples:  group({svm knn c45}),
>                    get_mean(train(cv(group({svm knn })),train(toy)))

>         Hyperparameter
>         group='all'           This parameter stores grouping type:
>                               'one_for_each', 'all' or 'separate' (default='all').
>                               When results of training and testing are
>                               output by a (grouped) algorithm,
>                               they are stored in the same group type given
>                               here. This is important for objects like chain,
>                               and get_mean which do not deal with data objects
>                               separately.

>             GROUP TYPE
>             'all':               each item is passed independently and separately
>                                  to all training objects, e.g
>                                    d=gen(toy); a=group({knn svm});
>                                    train(a,group({d d},'separate'))
>                                  gives 4 outputs.

>             'one_for_each':      the n^th data item is passed to the
>                                  n^th training object in an group object, e.g
>                                    d=gen(toy);g=group({d d},'one_for_each');
>                                    a=group({knn svm}); train(a,g)
>                                  gives 2 outputs.

>              'all':              passes all the data objects to a
>                                  single training object, e.g
>                                    d=gen(toy); a=group({knn svm}); r=train(a,d);
>                                    train(get_mean,group(r,'all'))
>                                  gives 1 ouput.

>        [NOTE: It is also possible to use the transpose operator with
>        group, e.g: r=group({ {svm svm} {knn knn} })' will give
>        the same result as group({ {svm knn} {svm knn}  })]