ORCID

0000-0001-7702-2564 (El Moudden)

Document Type

Article

Publication Date

2014

DOI

10.12988/ams.2014.42129

Publication Title

Applied Mathematical Sciences

Volume

8

Issue

50

Pages

2483-2496

Abstract

Classification and statistical learning by hidden markov model has achieved remarkable progress in the past decade. They have been applied in many areas like speech recognition and handwriting recognition. However, learning by Hidden Markov Model (HMM) is still restricted to supervised problems. In this paper, we propose a new learning method based on HMM techniques estimations, to built a model for classification. The approach consists of evaluation of the probability to belonging in one group, given the observations by a linear classifier. Our developed algorithm is based on discrete states and discrete observations cases of HMM. Experimental results show that the new method has strong performance.

Rights

© 2014 Badreddine Benyacoub et al. 

This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Original Publication Citation

Benyacoub, B., ElBernoussi, S., Zoglat, A., & El Moudden, I. (2014). Classification with hidden Markov model. Applied Mathematical Sciences, 8(50), 2483-2496. https://doi.org/10.12988/ams.2014.42129

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