Abstract
The main theme is to show that a one-hidden-layer neural network, which has learned a Bayesian discriminant function, can be used for estimating hidden Markov chains. The crucial point of the algorithm is the use of the logistic function as the activation of the output unit of the network. The network learns a single discriminant function, but converts it to the individual discriminant functions at all the steps.
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Ito, Y., Izumi, H., Srinivasan, C. (2014). Estimation of Hidden Markov Chains by a Neural Network. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_75
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