> The Spider Objects

>       ONE_CLASS_SVM svm object

>        A=ONE_CLASS_SVM(H) returns an svm object initialized with hyperparameters H.

>         Learns one class problems, e.g for novelty detection by learnign
>         the support of a density.

>        Hyperparameters, and their defaults
>         C=Inf                -- the soft margin C parameter
>         optimizer='default'  -- other choices={andre,quadprog,svmlight,libsvm}
>         nu = 0               -- bernhard's nu svm parameter
>         child=kernel         -- the kernel is stored as a member called "child"

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

>        Methods:
>         train, test, get_w
>         Example:
>             Assume only positive examples are available
>          d=gen(toy('l=100'));
>          d.X=d.X(d.Y==1,:); d.Y=d.Y(d.Y==1,:);   only have one class available
>          [r a]=train(param(one_class_svm('optimizer="quadprog"'),'nu', 2.^[-5:0.5:-1]),d);
>          loss(test(a,gen(toy)))

> | Reference : Estimating the support of a high-dimensional distribution

> | Author : B. Schölkopf , A. J. Smola , J. Platt , J. Shawe-Taylor and R. C. Williamson

> | Link : http://www.kernel-machines.org/papers/oneclass-tr.ps.gz