SPIDER The Spider Objects

LOSS object

     calculates the difference between the input X and the ouput Y depending
     on the specified loss type (for further information see below).
     The loss can be calculated in two ways. The first is to it by training,
     the second is to call the function with a data object as first
     parameter (loss(d,loss_type,param)). The results are stored in the Y
     part of the data object.
     Attributes (with defaults): 
         type='class_loss'  -- type of loss (class_loss,linear_loss...)
         param=[]           -- used parameters (can also be empty)
     LOSS              |   PARAMETERS & DESCRIPTION
     class_loss        -- zero/one loss, L(x,y)=1 if x=y, 0 otherwise
     confusion_matrix  -- matrix of [true-pos, false-pos; false-neg, true-neg]
     epsilon_loss      -- L(x,y)= |x-y|, if |x-y|>epsilon, 0 otherwise
     linear_loss       -- 1-norm, L(x,y)=|x-y|
     one_class_loss    -- for one-class, e.g novelty detection, etc.
     quadratic_loss    -- 2-norm, L(x,y)=|x-y|_2^2
     roc               -- receiver/operator characteristic
     roc50             -- receiver/operator characteristic, first n fps
     sensitivity       -- tp/(tp+fn)
     specificity       -- tn/(fp+tn)
     kernel            -- loss derived from kernel matrix (param) 
                       -- inner products in 'loss' space between examples.
     alignment         -- L(x,y)= sum(sum( (x*x') .* (y*y'))) / normalization