SPIDER The Spider Objects

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
    alpha                -- the weights   
    Xsv                  -- the Support Vectors  
    uv                   -- decomposition in output space  
    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/