Global Optimisation of Neural Network Models via Sequential Sampling

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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João de Freitas, Mahesan Niranjan, Arnaud Doucet, Andrew Gee


We propose a novel strategy for training neural networks using se(cid:173) quential sampling-importance resampling algorithms. This global optimisation strategy allows us to learn the probability distribu(cid:173) tion of the network weights in a sequential framework. It is well suited to applications involving on-line, nonlinear, non-Gaussian or non-stationary signal processing.