> The Spider Objects

>            KPLS object

>        A=kpls(C) returns a kernel partial least squares  object
>         object initialized with hyperparameters H.

>         Hyperparameters, and their defaults
>          nroflatent=5;	 maximum number of latent variables. depends also on
>                           epsilon
>          epsilon          stopping criteria - determines number of latent
>                           variables
>          epsilon2         stopping criteria for found directions

>         Model
>          a.dtraining=[]; data used for training
>          a.Kc=[];        centered kernel matrix
>          a.U=[];         found output directions
>          a.T=[];         found input directions
>          child 		 -- kernel
>          original;     --

>         Methods:

>          train, test

> | Reference : Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space

> | Author : Roman Rosipal , Leonard J. Trejo

> | Link : http://www.kernel-machines.org/jmlr/