> The Spider Objects

>            LOSS object

>        a=loss(lossType,param)

>          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)

>          Methods:
>              train,test,calc

>          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