Hidden Markov models for speech recognition
Technometrics
Smooth on-line learning algorithms for hidden Markov models
Neural Computation
On how pachycondyla apicalis ants suggest a new search algorithm
Future Generation Computer Systems
Learning mixture models using a genetic version of the EM algorithm
Pattern Recognition Letters
BYY harmony learning, structural RPCL, and topological self-organizing on mixture models
Neural Networks - New developments in self-organizing maps
Genetic-Based EM Algorithm for Learning Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved genetic algorithm for optimal design of fuzzy classifier
International Journal of Computer Applications in Technology
A study on multidisciplinary collaborative optimisation based on an improved PSO
International Journal of Computer Applications in Technology
Constraints in particle swarm optimization of hidden markov models
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Semantic similarity-based PageRank using wordnet
International Journal of Computer Applications in Technology
A simple quantum-inspired bee colony algorithm for discrete optimisation problems
International Journal of Computer Applications in Technology
International Journal of Computer Applications in Technology
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The conventional method for parameter estimation of HMMs uses the Baum-Welch (BW) algorithm. However, the BW algorithm is highly sensitive to initial values of the model parameters. In this paper, we propose an Ant Colony Optimisation (ACO)-based BW algorithm (ACO-BW) for estimating the parameters of HMMs. Our approach benefits from the properties of ACO algorithms and the BW algorithm by combination of both into a single procedure. The improved ACO algorithm provides a new model of artificial ants which are characterised by a relatively simple but efficient strategy of prey search. This is performed by parallel local searches on hunting sites with sensitivity to successful sites. The ACO-BW algorithm also maintains the monotonic convergence property of the BW algorithm. Experimental results show that ACO-BW obtains better values for the likelihood function as well as higher recognition accuracy than that of the HMMs trained by other existing methods.