DocumentCode :
3072403
Title :
Using Randomized Search Algorithms to Estimate HMM Learning Parameters
Author :
Nath, Ravindra ; Jain, Renu
Author_Institution :
Dept. of Comput. Sci. & Eng., CSJM Univ., Kanpur
fYear :
2009
fDate :
6-7 March 2009
Firstpage :
159
Lastpage :
164
Abstract :
Many complex problems like speech recognition, bioinformatics, climatology, control and communication are solved using hidden Markov models (HMM). Mostly, optimization problems are modeled as HMM learning problem in which HMM parameters are either maximized or minimized. In general, Baum-Welch Method (BW) is used to solve HMM learning problem giving only local maxima/minima in exponential time. In this paper, we have modeled HMM learning problem as a discrete optimization problem such that randomized search methods can be used to solve the learning problem. We have implemented metropolis algorithm (MA) and simulated annealing algorithm (SAA) to solve the discretized HMM learning problem. A comparative study of randomized algorithms with the Baum Welch method to estimate the HMM learning parameters has been made. The metropolis algorithm is found to reach maxima in minimum number of transactions as compared to the Baum-Welch and simulated annealing algorithms.
Keywords :
hidden Markov models; learning (artificial intelligence); parameter estimation; search problems; simulated annealing; Baum-Welch Method; HMM learning; discrete optimization problem; hidden Markov models; metropolis algorithm; parameter estimation; randomized search algorithms; simulated annealing algorithm; Bioinformatics; Computer science; Educational institutions; Hidden Markov models; Parameter estimation; Probability distribution; Simulated annealing; Speech recognition; State estimation; Stochastic processes; Baum-Welch; Hidden Markov Models; Metropolis Algorithm; Simulated Annealing Algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advance Computing Conference, 2009. IACC 2009. IEEE International
Conference_Location :
Patiala
Print_ISBN :
978-1-4244-2927-1
Electronic_ISBN :
978-1-4244-2928-8
Type :
conf
DOI :
10.1109/IADCC.2009.4808999
Filename :
4808999
Link To Document :
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