neuroevolution mechanism for hidden markov model
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ID: 229829
2011
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Abstract
Hidden Markov Model (HMM) is a statistical model based on probabilities. HMM is becoming one of the major models involved in many applications such as natural language<br />processing, handwritten recognition, image processing, prediction systems and many more. In this research we are concerned with finding out the best HMM for a certain application domain. We propose a neuroevolution process that is based first on converting the HMM to a neural network, then generating many neural networks at random where each represents a HMM. We proceed by<br />applying genetic operators to obtain new set of neural networks where each represents HMMs, and updating the population. Finally select the best neural network based on a fitness function.
| Reference Key |
hewahi2011brain:neuroevolution
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| Authors | ;Nabil M. Hewahi |
| Journal | isprs annals of the photogrammetry, remote sensing and spatial information sciences |
| Year | 2011 |
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