> The Spider Objects

>              MULTI_RR (possibly multiple output) ridge regression object

>        A=MULTI_RR(H) returns an object initialized with hyperparameters H.

>        Performs ridge regression independently for each column we wish
>        to learn, which only means inverting a single (kernel) matrix

>        Hyperparameters, and their defaults
>         ridge=1e-13;   -- a ridge on the kernel
>         child=kernel;  -- the kernel is stored as a member called "child"
>         use_kernels=1; -- if this is set to 0 a linear ridge regression or
>                           an empirical kernel map (useful for reduced sets
>                           of centers)
>         indices = []   -- indices of a reduced set of centers to be used
>                              for learning. ([] means use all training set)
>         use_b=1        -- find a threshold, otherwise fix to 0

>        Model
>         alpha          -- the weights
>         Xsv            -- centers

>        Methods:
>         train, test, get_w
>         loo : calculate leave one out predictions (with empirical kernel, or linear)

> | Reference : Ridge Regression Learning Algorithm in Dual Variables

> | Author : C. Saunders , A. Gammerman and V. Vovk

> | Link : http://citeseer.ist.psu.edu/saunders98ridge.html