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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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: Mana, N., Schwenker, F., Trentin, E. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2012. Lecture Notes in Computer Science(), vol 7477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33212-8_8
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DOI: https://doi.org/10.1007/978-3-642-33212-8_8
Publisher Name: Springer, Berlin, Heidelberg
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