D-optimal Bayesian Interrogation for Parameter and Noise Identification of Recurrent Neural Networks
Clinical Orthopaedics and Related Research(2008)
摘要
We introduce a novel online Bayesian method for the identification of a
family of noisy recurrent neural networks (RNNs). We develop Bayesian active
learning technique in order to optimize the interrogating stimuli given past
experiences. In particular, we consider the unknown parameters as stochastic
variables and use the D-optimality principle, also known as `\emph{infomax
method}', to choose optimal stimuli. We apply a greedy technique to maximize
the information gain concerning network parameters at each time step. We also
derive the D-optimal estimation of the additive noise that perturbs the
dynamical system of the RNN. Our analytical results are approximation-free. The
analytic derivation gives rise to attractive quadratic update rules.
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关键词
bayesian method,information gain,optimal estimation,dynamic system,active learning,evolutionary computing,information theory,recurrent neural network
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