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

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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.

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较早

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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