In the ME framework, the conditional probability of predicting an outcome o given a history h is defined as follows: where is a binary-valued feature function, λ i is the weighting parameter of f i (h, o), k is the number of features, and Z λ (h) is a normalization factor for Σ o p λ (o|h)=1.
the probability p λ (o|h) is calculated by the weighted sum of active features.