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IAPR Workshop on Artificial Neural Networks in Pattern Recognition

ANNPR 2012: Artificial Neural Networks in Pattern Recognition pp 82–92Cite as

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

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

  • Marco Bongini22 &
  • Edmondo Trentin22 
  • Conference paper
  • 1279 Accesses

  • 1 Citations

Part of the Lecture Notes in Computer Science book series (LNAI,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

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References

  1. Freno, A.: JProGraM - PRObabilistic GRAphical Models in Java (2009), http://www.dii.unisi.it/~freno/JProGraM.html

  2. Freno, A., Trentin, E.: Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models. Springer (2011)

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  3. 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)

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  4. 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)

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  5. 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)

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Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Siena, Siena, Italy

    Marco Bongini & Edmondo Trentin

Authors
  1. Marco Bongini
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  2. Edmondo Trentin
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Editor information

Editors and Affiliations

  1. Fondazione Bruno Kessler (FBK), 38123, Trento, Italy

    Nadia Mana

  2. Institute of Neural Information Processing, University of Ulm, 89069, Ulm, Germany

    Friedhelm Schwenker

  3. Dipartimento di Ingegneria dell’Informazione, Università di Siena, 53100, Siena, Italy

    Edmondo Trentin

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

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

  • Print ISBN: 978-3-642-33211-1

  • Online ISBN: 978-3-642-33212-8

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