This paper develops a maximum pseudo-likelihood algorithm for learning the structure of the dynamic extension of Hybrid Random Field introduced in the companion paper . The technique turns out to be a viable method for capturing the statistical (in)dependencies among the random variables within a sequence of patterns. Complexity issues are tackled by means of adequate strategies from classic literature on probabilistic graphical models. A preliminary empirical evaluation is presented eventually.
|Titolo:||Towards a novel probabilistic graphical model of sequential data: a solution to the problem of structure learning and an empirical evaluation|
|Citazione:||Bongini, M., & Trentin, E. (2012). Towards a novel probabilistic graphical model of sequential data: a solution to the problem of structure learning and an empirical evaluation. In Proceedings of the 5th IAPR Workshop on Artificial Neural Networks in Pattern Recognition (pp.82-92). Springer-Verlag.|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|
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