GLOBAL OPTIMISATION OF NEURAL NETWORK MODELS VIA SEQUENTIAL SAMPLE-IMPORTANCE RESAMPLING
JFG de Freitas, SE Johnson, M Niranjan and AH Gee
We propose a novel strategy for training neural networks using sequential Monte Carlo algorithms. This global optimisation strategy allows us to learn the probability distribution of the network weights in a sequential framework. It is well suited to applications involving on-line, nonlinear or non-stationary signal processing. We show how the new algorithms can outperform extended Kalman filter (EKF) training.
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