Titlebook: Hybrid Random Fields; A Scalable Approach Antonino Freno,Edmondo Trentin Book 2011 Springer Berlin Heidelberg 2011 Bayesian Networks.Data

甜食

Applications,it the flexibility of HRFs in modeling independence structures, as well as the scalability of algorithms for learning HRFs, in order to tackle real-world problems. Improvements over the traditional approaches, both in terms of prediction accuracy and computational efficiency, are sought.

臥虎藏龍

較早

Introducing Hybrid Random Fields: Discrete-Valued Variables,s problems involving several variables. For example, the link-prediction application we will describe in Section?6.3.4.3, which involves 1,682 variables, is such that the Markov random field applied to it contains 16,396 feature functions, with the corresponding weights.

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