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Modified Baum Welch Algorithm for Hidden Markov Models with Known Structure

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Intelligent Human Systems Integration 2019 (IHSI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 903))

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Abstract

Hidden Markov Models (HMMs) are widely used in speech and handwriting recognition, behavior prediction in traffic, time series analysis, biostatistics, image and signal processing, and many other fields. For some applications in those real world problems, a-priori knowledge about the structure of the HMM is available. For example the shape of the state transition matrix and/or the observation matrix might be given. We might know that some entries in these matrices are equal and others are zero. For training such a model, we have two options: use the common Baum Welch Algorithm (BWA) and enforce the given structure after training or modify the BWA to enforce it during training. This paper shows several approaches for modifying the BWA and compares the results of all training methods.

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References

  1. Wendemuth, A.: Grundlagen der stochastischen Sprachverarbeitung. Oldenbourg Verlag, München (2004)

    Book  Google Scholar 

  2. Fink, G.A.: Markov Models for Pattern Recognition, 2nd edn. Springer, London (2014)

    Book  Google Scholar 

  3. Kuge, N., Yamamura, T., Shimoyama, O., Liu, A.: A driver behavior recognition method based on a driver model framework. SAE Technical Paper, Technical report (2000)

    Google Scholar 

  4. Streubel, T., Hoffmann, K.H.: Prediction of driver intended path at intersections. In: Intelligent Vehicles Symposium Proceedings, 8–11 June 2014, pp. 134–139. IEEE, Dearborn (2014)

    Google Scholar 

  5. Knab, B.: Erweiterung von Hidden-Markov-Modellen zur Analyse ökonomischer Zeitreihen. Ph.D. dissertation, Universit ̈at zu Köln (2000)

    Google Scholar 

  6. Hassan, M.R., Nath, B.: Stock market forecasting using hidden Markov model: a new approach. In: 5th International Conference on Intelligent Systems Design and Applications (ISDA 2005), pp. 192–196 (2005)

    Google Scholar 

  7. Schliep, A., Schönhuth, A., Steinhoff, C.: Using hidden Markov models to analyze gene expression time course data. Bioinformatics 19(1), i255–i263 (2003)

    Article  Google Scholar 

  8. Holzmann, H., Munk, A., Suster, M., Zucchini, W.: Hidden Markov models for circular and linear-circular time series. Environ. Ecol. Stat. 13(3), 325–347 (2006)

    Article  MathSciNet  Google Scholar 

  9. Li, J., Najmi, A., Gray, R.M.: Image classification by a two-dimensional hidden Markov model. IEEE Trans. Signal Process. 48(2), 517–533 (2000)

    Article  Google Scholar 

  10. Dash, D.P., Kolekar, M.H.: Epileptic seizure detection based on EEG signal analysis using hierarchy based Hidden Markov Model. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1114–1120 (2017)

    Google Scholar 

  11. Rabiner, L.R., Juang, B.-H.: An introduction to hidden Markov models. IEEE ASSP Mag. 3(1), 4–16 (1986)

    Article  Google Scholar 

  12. Li, X., Parizeau, M., Plamondon, R.: Training hidden Markov models with multiple observations - a combinatorial method. Trans. PAMI 22(4), 371–377 (2000)

    Article  Google Scholar 

  13. Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bull. 1(6), 80–83 (1945)

    Article  Google Scholar 

  14. McCornack, R.L.: Extended tables of the Wilcoxon matched pair signed rank statistic. J. Am. Stat. Assoc. 60(311), 864–871 (1965)

    Article  MathSciNet  Google Scholar 

  15. Pearson, E.S., Hartley, H.O.: Biometrika Tables for Statisticians, vol. 1. Cambridge University Press, New York (1966)

    MATH  Google Scholar 

  16. Owen, D.B.: Handbook of Statistical Tables. Addison-Wesley, Reading (1962)

    MATH  Google Scholar 

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Acknowledgments

This research was supported by the European Social Fund and the Free State of Saxony under Grant No. 100269974.

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Authors and Affiliations

  1. Chemnitz University of Technology, Straße der Nationen 62, 09111, Chemnitz, Germany

    Kim Schmidt & Karl Heinz Hoffmann

Authors
  1. Kim Schmidt
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  2. Karl Heinz Hoffmann
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Corresponding author

Correspondence to Kim Schmidt .

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Editors and Affiliations

  1. University of Central Florida, Orlando, FL, USA

    Waldemar Karwowski

  2. Institute for Advanced Systems Engineering, University of Central Florida, Orlando, FL, USA

    Tareq Ahram

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Schmidt, K., Hoffmann, K.H. (2019). Modified Baum Welch Algorithm for Hidden Markov Models with Known Structure. In: Karwowski, W., Ahram, T. (eds) Intelligent Human Systems Integration 2019. IHSI 2019. Advances in Intelligent Systems and Computing, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-030-11051-2_75

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