> The Spider Objects

>            SVR object

>        A=SVR(H) returns a svr object initialized with hyperparameters H.

>         Hyperparameters, and their defaults

>          C=Inf;               -- the soft margin C parameter
>          optimizer='andre';  -- other choices={quadprog,andre,libsvm,svmtorch,sparse}
>          alpha_cutoff=-1;     -- keep alphas with abs(a_i)>alpha_cutoff
>          nu = 0;              -- nu parameter of a nu svr (different
>                                  from zero implies the nu-SVR is used
>                                  otherwise, the epsilon-SVR is used)
>          child=kernel;        -- the kernel is stored as a member called "child"
>          epsilon=0.1;         -- the value of epsilon in the epsilon
>                                  insensitive loss function during learning
>          use_signed_output=0; -- set to 1 implies that the svr is used
>                                  in classification (+1/-1 outputs), set to 0
>                                  implies that the svr is used in regression
>         Model

>          alpha                -- the weights
>          b0                   -- the threshold
>          Xsv                  -- the Support Vectors

>         Methods:

>          train, test, get_w

> | Reference : A tutorial on Support Vector Regression

> | Author : Alex J. Smola , Bernhard Schölkopf

> | Link : http://citeseer.ist.psu.edu/smola98tutorial.html