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Published in:
2011 | OriginalPaper | Chapter
Applications
Authors : Antonino Freno, Edmondo Trentin
Published in: Hybrid Random Fields
Publisher: Springer Berlin Heidelberg
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As we have said, hybrid random fields are not meant just as a general graphical model with nice theoretical properties, featuring algorithms for inference and learning over discrete and continuous variables. Above all, they are expected to reveal useful. This means that our ultimate goal is to exploit 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.