Without loss of generality, let us aim at learning a prediction function f : X → Y that maps input x ∈ X to an output y ∈ Y with latent structural information h ∈ H. The decision function is of the following form: where Ψ(x, y, h) is the set of feature functions defined jointly over the input x, the output y and the latent variable h. w is the parameter of the model.
the value of the feature (which is computed in an accumulative manner) cannot increase as the candidate is extended with a new term at any search step.