DocumentCode
116700
Title
Classification of multidimensional observation sequences described by Hidden Markov Models
Author
Gultyaeva, T.A. ; Kokoreva, V.V.
Author_Institution
Novosibirsk State Tech. Univ., Novosibirsk, Russia
fYear
2014
fDate
2-4 Oct. 2014
Firstpage
556
Lastpage
561
Abstract
This article analyses several approaches to classification of multidimensional observation sequences described by Hidden Markov Models. It demonstrates good efficiency of method based on HMM parameter derivatives when competing classes are close in some way.
Keywords
hidden Markov models; image classification; image sequences; HMM parameter derivatives; hidden Markov models; multidimensional observation sequences; Hidden Markov models; Markov processes; Probability density function; Random processes; Speech recognition; Support vector machine classification; Training; Hidden Markov Models; classification of sequences; derivatives;
fLanguage
English
Publisher
ieee
Conference_Titel
Actual Problems of Electronics Instrument Engineering (APEIE), 2014 12th International Conference on
Conference_Location
Novosibirsk
Print_ISBN
978-1-4799-6019-4
Type
conf
DOI
10.1109/APEIE.2014.7040746
Filename
7040746
Link To Document