> The Spider Objects

>            MUTINF object

>        A=MUTINF(C,H) returns a mutinf object initialized with hyperparameters H given a classifier C.

>        Performs feature selection by means of the mutual information between attributes and the target.
>        If there is a given number of features to select, this number of features is selected according to
>        the ranking based upon the mutatul information.

>        If no number of features to select is given, a probalistic forward selection is used:
>        A feature is selected, if P(I > epsilon) >= 95 , where I is the mutual information between the attribute
>        and the target. (c.f. Zaffalon, Hutter, Robust Feature Selection by Mutual Information Distributions)
>        Here epsilon is set to the mutual information that exists between a normally distributed random feature
>        and the target.

>        Hyperparameters, and their defaults
>         feat=[]		-- number of features to be selected
>         method='regression'  -- use feature selection for regression or classification
>         c                    -- learning algorithm (e.g. svm)

>        Model
>         rank                 -- the ranking of the features
>         child	        -- learning algorithm (e.g. svm)

>        Methods:
>         train, test, get_w

>        Example:
>         a=mutinf(svm); a.method='classification';a.feat=10;
>         [r,a]=train(a,toy); loss(test(a,toy))

> | Reference : Robust Feature Selection by Mutual Information Distributions

> | Author : Marco Zaalon and Marcus Hutter

> | Link : http://citeseer.ist.psu.edu/566806.html