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 [5]. 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.
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 [10.1007/978-3-642-33212-8_8].
Towards a novel probabilistic graphical model of sequential data: a solution to the problem of structure learning and an empirical evaluation
Trentin, Edmondo
2012-01-01
Abstract
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 [5]. 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.
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https://hdl.handle.net/11365/1007279