Towards a Novel Probabilistic Graphical Model of Sequential Data: A Solution to the Problem of Structure Learning and an Empirical Evaluation

  • Marco Bongini
  • Edmondo Trentin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7477)

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.

Keywords

Probabilistic graphical model Hidden Markov model Hybrid Random Field Sequence Classification 

References

  1. 1.
    Freno, A.: JProGraM - PRObabilistic GRAphical Models in Java (2009), http://www.dii.unisi.it/~freno/JProGraM.html
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    Freno, A., Trentin, E.: Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models. Springer (2011)Google Scholar
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    Freno, A., Trentin, E., Gori, M.: Scalable Pseudo-Likelihood Estimation in Hybrid Random Fields. In: Elder, J.F., Fogelman-Souli, F., Flach, P., Zaki, M. (eds.) Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2009), pp. 319–327. ACM (2009)Google Scholar
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    Freno, A., Trentin, E., Gori, M.: Scalable Statistical Learning: A Modular Bayesian/Markov Network Approach. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2009), pp. 890–897. IEEE (2009)Google Scholar
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    Trentin, E., Bongini, M.: Towards a Novel Probabilistic Graphical Model of Sequential Data: Fundamental Notions and a Solution to the Problem of Parameter Learning. In: Mana, N., Schwenker, F., Trentin, E. (eds.) ANNPR 2012. LNCS (LNAI), vol. 7477, pp. 72–81. Springer, Heidelberg (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marco Bongini
    • 1
  • Edmondo Trentin
    • 1
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversità degli Studi di SienaSienaItaly

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