Titlebook: Markov Logic; An Interface Layer f Pedro Domingos,Daniel Lowd Book 2009 Springer Nature Switzerland AG 2009

灰心喪氣

玉米

Inference,lways answer probabilistic queries using standard Markov network inference methods on the instantiated network. However, due to the size and complexity of the resulting network, this is often infeasible. Instead, the methods we discuss here combine probabilistic methods with ideas from logical infer

高原

Learning,anually specifying the complete model. We begin by discussing weight learning, in which we try to find the formula weights that maximize the likelihood or conditional likelihood of a relational database. Our methods for solving this problem are based on convex optimization but take into account the

lobster

blight

Applications,cover eight applications: collective classification, social network analysis, entity resolution, information extraction, coreference resolution, robot mapping, link-based clustering, and semantic network extraction.

Calibrate

Discrete

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