> The Spider Objects

>       KDE kernel dependency estimation - for general input-output relations

>        This version does an eigendecomposition of the output kernel L
>        and learns targets by learning each orthogonal direction independently.
>        The pre-image algorithm is just to choose closest pre-image from
>        training set.

>        A=KDE(H) returns a kde object initialized with hyperparameters H.

>        Hyperparameters, and their defaults
>         feat=[]               -- number of KPCA features to train on,
>                                 default take eigenvalues with lambda>lambda_max/10000
>         ridge=1e-5           -- regularization on input kernel
>         child=kernel         -- the kernel for inputs stored as member "child"
>         ok=kernel            -- the kernel for outputs
>         output_preimage=0    -- output index from training sample of  preimage
>                                 instead of actual label
>         use_pca=1            -- can remove decorrelation part for quick & dirty
>                                 approximation (also allows to handle non-Mercer
>                                 kernels)
>        Model
>         alpha                -- the weights
>         Xsv                  -- the Support Vectors
>         uv                   -- decomposition in output space

>        Methods:
>         train, test

> | Reference : Kernel Dependency Estimation

> | Author : Jason Weston, Olivier Chapelle, Andre Elisseeff, Bernhard Schoelkopf and Vladimir Vapnik

> | Link : http://www.kyb.tuebingen.mpg.de/bs/people/weston/