Optimizing Hidden Markov Models Using Genetic Algorithms and Artificial Immune Systems
نویسندگان
چکیده
Hidden Markov Models are widely used in speech recognition and bioinformatics systems. Conventional methods are usually used in the parameter estimation process of Hidden Markov Models (HMM). These methods are based on iterative procedure, like BaumWelch method, or gradient based methods. However, these methods can yield to local optimum parameter values. In this work, we use artificial techniques such as Artificial Immune Systems (AIS) and Genetic Algorithms (GA) to estimate HMM parameters. These techniques are global search optimization techniques inspired from biological systems. Also, the hybrid between genetic algorithms and artificial immune system was used to optimize HMM parameters.
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