> The Spider Objects

>       Bayesian model selection for SVM/SVR following Kwok et al. ,

>        Proceedings of the European Symposium on Artificial Neural Networks (ESANN), pp.177-182, Bruges, Belgium, April 1999.
>        for selecting the parameter C and ranking models

>        inputs:
>          A		    - SVM/SVR

>        hyperparameters and their defaults:
>          use_balanced_C=0  - adapt balanced C for SVM
>          type='L1'	      - use l1-SVM

>        Methods:
>         train    - optimizes soft margin parameter for given data
>         test     -

>        outputs:
>          pbest      - best parameter C
>          posterior  - posterior probability for the model (given the data)
>        Example:

>         [r,a]=train(bayessel(svm),gen(toy))
>         [a.pbest,a.posterior]

> | Reference : (The Evidence Framework Applied to SVM / Bayesian SVR)

> | Author : Kwok et al

> | Link : Proceedings of the European Symposium on Artificial Neural Networks (ESANN), pp.177-182