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In this letter we re{examine the emergence of plateaus or symmetric phases in on{line learning of a committee machine. We propose a simple matrix{ update in order to avoid the symmetric phase. Simulations show that the length of the plateaus can be considerably decreased. Annealing of the learning rate can then be applied much earlier, making on{line(More)
We introduce a local adaptation process in the orthogonal least squares (OLS) learning algorithm for the selection of radial basis function (RBF) networks. Using simulation results, we show that the proposed algorithm can find significantly better subset models than the OLS algorithm.
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