Paper title: A Proposed Method for Pattern Classification with HMM in the Context of Supervised Learning
Published in: Issue 1, (Vol. 9) / 2015Download
Publishing date: 2015-03-31
Pages: 50-57
Author(s): PENTIUC Ştefan Gheorghe , ŞOIMAN Ştefania - Iuliana
Abstract. HMM was used in solving difficult problems in pattern recognition such as speech recognition, handwriting, postures and gestures recognition. This paper presents a proposed method based on HMM for a more general problem of pattern recognition, namely one in which the patterns are represented by points in a space with p dimensions. The first part of the paper is focused on the main elements of HMM theory. The following provides a formalism for the automatic classification of patterns in the context of supervised learning and the method by which this can be solved with HMM. The proposal of methodology is validated using GPL Jahmm library that has been extended with a new class for evaluating the performances of pattern classification with HMM.
Keywords: Hidden Markov Models, Pattern Recognition, Supervised Learning, Pattern Classification, Jahmm Library, Java
References:

1. Rabiner, L.R. (1989). A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE.

2. Bouchard G., Triggs B.(2004) The Trade-Off Between Generative snd Discriminative Classifiers, International Conference on Computational Statistics, pp. 721-728.

3. Sanguansat S, Asdornwised W, Jitapunkul S.(2004) Online Thai handwritten character recognition using hidden Markov models and support vector machines.

4. Baum, L.E. (1972). An Inequality and Associated Maximization Technique in Statistical Estimation of Probabilistic Functions of a Markov Process. Inequalities.

5. Forney G.D. (1973). The Viterbi Algoritm, Proc. IEEE, pp.268– 278.

6. Theodoridis S, Koitroimbas K.(2006) Pattern recognition, Third Edition, pp.443-469.

7. Jean-Marc François, Jahmm v0.6.1 User Guide, April 20, 2006, available on December 2014 at https://jahmm.googlecode.com/files/jahmm-0.6.1- userguide.pdf

8. Moustafa K.A., Cheriet M., Ching Suen. (2004). Classification of Time-Series Data Using a Generative/Discriminative Hybrid, IEEE Proc. of IWFHR, pp. 51 – 56.

9. Zhang Y., Guangshun Shi, Wang K. (2010) A SVM-HMM Based Online Classifier for Handwritten Chemical Symbols, ICPR.

10. Schipor Ovidiu-Andrei, Pentiuc Stefan-Gheorghe, Schipor Maria-Doina (2010) Improving Computer Based Speech Therapy Using a Fuzzy Expert System, COMPUTING AND INFORMATICS Volume: 29, Issue: 2, pp. 303-318

11. Vatavu Radu-Daniel, Pentiuc Stefan-Gheorghe (2008) MultiLevel Representation of Gesture as Command for Human Computer Interaction, Computing And Informatics Volume: 27 Issue: 6, pp. 837-851

12. Pentiuc Stefan-Gheorghe, Purdila Vasile (2013) Classification of Classifiers in Supervised Learning Pattern Recognition, Journal of Applied Computer Science & Mathematics, Issue 15, pp.23-26

13. Duda R.O., Hart P.E. Stork ,D. G. (2001) Pattern Classification, Wiley Interscience

14. Kostinger, M.; Hirzer, M.; Wohlhart, P.; Roth, P.M.; Bischof, H. (2012) "Large scale metric learning from equivalenceconstraints," Computer Vision and Pattern Recognition (CVPR), IEEE Conference on , vol., no., pp.2288,2295, 16-21 June 2012

15. Tiliute, Doru E. Security of mobile ad hoc wireless networks: a brief survey. Advances in Electrical and Computer Engineering, 2007, 7.2: 37-40.

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