6. The method of claim 5 , further comprising smoothing the inter-context scores and intra-context scores by a first value p and a second value m so that determining the inter-context score is based on a summing over each context value in the dataset values equal to: 
 (( n c,f +p*m )*( n f −n c,f +p*m ))/(( n f +m )*( n f −1 +m )); and determining the intra-context score based on a summing over each context value in the dataset values equal to: 
 (( n c,f +p*m )*( n f −1 +p*m ))/(( n f +m )*( n f −1 +m )).
6. The method of claim 5 , further comprising smoothing the inter-context scores and intra-context scores by a first value p and a second value m so that determining the inter-context score is based on a summing over each context value in the dataset values equal to: 
 (( n c,f +p*m )*( n f −n c,f +p*m ))/(( n f +m )*( n f −1 +m )); and determining the intra-context score based on a summing over each context value in the dataset values equal to: 
 (( n c,f +p*m )*( n f −1 +p*m ))/(( n f +m )*( n f −1 +m )). 7. The method of claim 6 , further comprising: grouping the feature values into disjoint sets of feature values; and determining, for each disjoint set of feature values, a corresponding first value p and a correspond second value m; wherein the first values p differ for at least two disjoint sets of feature values, and the second values m differ for at least two disjoint sets of feature values.