Classification with hidden Markov model
Badreddine Benyacoub, Souad Elbernoussi, Abdelhak Zoglat, Ismail El Moudden · Applied Mathematical Sciences · 2014
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.