Abstract
The method for classification performance improvement using hidden Markov models (HMM) is proposed. The k-nearest neighbors (kNN) classifier is used in the feature space produced by these HMM. Only the similar models with the noisy original sequences assumption are discussed. The research results on simulated data for two-class classification problem are presented.
Chapter PDF
Similar content being viewed by others
References
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proc. IEEE 77(2), 257–285 (1989)
Cappé, O.: Ten years of HMMs. CNRS, LTCI & ENST, Dpt. TSI, http://perso.telecom-paristech.fr/~cappe/docs/hmmbib.html
Mottl, V.V., Muchnik, I.B.: Hidden Markov Models in Structural Signal Analysis Moscow, Russia (1999) (in Russian)
Zagorujko, N.G.: Applied methods of analysis of data and knowledge. Novosibirsk, Russia (1999) (in Russian)
Chen, L., Man, H.: Combination of Fisher Scores and Appearance Based by Features For Face. In: Proc. of the 2003 ACM SIGMM Workshop on Biometrics Methods and Applications, Berkeley, California, USA, pp. 74–81 (2003)
Aran, O., Akarun, L.: Recognizing Two Handed Gestures with Generative, Discriminative and Ensemble Methods Via Fisher Kernels. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds.) MRCS 2006. LNCS, vol. 4105, pp. 159–166. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Popov, A.A., Gultyaeva, T.A. (2011). The Classification of Noisy Sequences Generated by Similar HMMs. In: Kuznetsov, S.O., Mandal, D.P., Kundu, M.K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2011. Lecture Notes in Computer Science, vol 6744. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21786-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-21786-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21785-2
Online ISBN: 978-3-642-21786-9
eBook Packages: Computer ScienceComputer Science (R0)