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The extended Kalman filter has been successfully applied to the feedforward and the recurrent neural network training. Recently introduced derivative-free filters (unscented Kalman filter and divided difference filter) outperform the extended Kalman filter in nonlinear state estimation. In the parameter estimation of the feedforward neural networks UKF and(More)
Coreference resolution is the process of determining whether two expressions in natural language refer to the same entity in the world. We adopt machine learning approach using decision tree to a coreference resolution of general noun phrases in unrestricted text based on well defined features. We also use approximate matching algorithms for a string match(More)
We consider the problem of recurrent neural network training as a Bayesian state estimation. The proposed algorithm uses Gaussian sum filter for nonlinear, non-Gaussian estimation of network outputs and synaptic weights. The performances of the proposed algorithm and other Bayesian filters are compared in noisy chaotic time series long-term prediction
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