14. An apparatus as in claim 13 , further comprising calculating a quantity: 
 d j =f j −f i (4) for each category node j, where i is an index of a true category, and then computing 
 d max =max j d j =d ∞ ( x )  (5) where a sign of d max indicates whether the pattern was mis-classified and thereby automatically determining the quantity d ∞ (x), and whereby if a pattern is misclassified, a probability of entering the resampling buffer is set proportional to d max , where d max ∈[−1,1] if the discriminant functions satisfy the condition f i ∈[0,1], and where there is further included a baseline acceptance probability defined by 
 P accept =θ( d max )( P baseline probability +( d max /scale)*(1 −P baseline probability ))  (6) where d max is a Heaviside (step) function.
14. An apparatus as in claim 13 , further comprising calculating a quantity: 
 d j =f j −f i (4) for each category node j, where i is an index of a true category, and then computing 
 d max =max j d j =d ∞ ( x )  (5) where a sign of d max indicates whether the pattern was mis-classified and thereby automatically determining the quantity d ∞ (x), and whereby if a pattern is misclassified, a probability of entering the resampling buffer is set proportional to d max , where d max ∈[−1,1] if the discriminant functions satisfy the condition f i ∈[0,1], and where there is further included a baseline acceptance probability defined by 
 P accept =θ( d max )( P baseline probability +( d max /scale)*(1 −P baseline probability ))  (6) where d max is a Heaviside (step) function. 15. An apparatus as in claim 14 , wherein scale is set to 1 so that the probability of acceptance is ∈[P baseline probability , 1].