constrained conditional models造句

Other algorithms and models for structured prediction include inductive logic programming, case-based reasoning, structured SVMs, Markov logic networks and constrained conditional models.
The key advantage of using an ILP solver for solving the optimization problem defined by a constrained conditional model is the declarative formulation used as input for the ILP solver, consisting of a linear objective function and a set of linear constraints.
Constrained conditional models form a learning and inference framework that augments the learning of conditional ( probabilistic or discriminative ) models with declarative constraints ( written, for example, using a first-order representation ) as a way to support decisions in an expressive output space while maintaining modularity and tractability of training and inference.
Roth has worked on probabilistic reasoning ( including its complexity and probabilistic lifted inference ), Constrained Conditional Models ( ILP formulations of NLP problems ) and constraints driven learning, part-based ( constellation ) methods in object recognition, response based Learning, He has developed NLP and Information extraction tools that are being used broadly by researchers and commercially, including NER, coreference resolution, wikification, SRL, and ESL text correction.
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