SPIDER The Spider Objects

FISHER object

   A=FISHER(H) returns a fisher object initialized with hyperparameters H. 
   Calculates a Fisher/Correlation score for each feature to implement
    feature selection.
   Hyperparameters, and their defaults
    feat=[]              -- number of features
    output_rank=1        -- set to 1 if only the feature ranking matters
                            (does not perform any classification on the data)
                            otherwise performs classification using
                            weights given by individual correlation scores
    method=2             -- useful only for multi-class. Set the how to combine
                            the score of different one-vs-rest fisher's score. 
                            (2 = take the sum, 1 = take the max)
    w                    -- the weights
    b0                   -- the threshold (when using all features)
    rank                 -- the ranking of the features
    d                    -- training set
    train, test, get_w 
   d=gen(toy); a=fisher; a.feat=10; a.output_rank=1;[r,a]=train(a,d);
   a.rank    - lists the chosen features in  order of importance
    To use for Furey et al. method (e.g correlation coefficients + svm) 
    use:  chain(fisher('output_rank=1'),svm)

Reference : Neural Networks for Pattern Recognition
Author : C. Bishop
Link : http://www.amazon.com/exec/obidos/tg/detail/-/0198538642/qid=1080909371/sr=8-1/ref=pd_ka_1/002-6279399-2828812?v=glance&s=books&n=507846