We minimize the following loss function for the ML criterion training of CRFs: To reduce over-fitting, the Maximum a Posteriori (MAP) criterion of parameters λ, that is, arg max λ k log p(λ|y * k , x k ) ∝ k log p(y * k |x k ; λ)p(λ), is now the most widely used CRF training criterion.
we minimize the following loss function for the MAP criterion training of CRFs: There are several possible choices when selecting a prior distribution p(λ).