A hybrid learning method for multilayer neural networks

A Newton learning approach for training a multilayer neural network is provided based on an efficient derivation of Hessian matrix of the network. Since the Newton's method converges almost quadratically, the convergence performance is improved. A hybrid learning method is developed in conjunction with the conventional backpropagation algorithm. Its performance is demonstrated by the classical XOR and parity problems.<
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