Efficient Parallel Learning Algorithms for Neural Networks
Alan Kramer, Alberto L. Sangiovanni-Vincentelli · Neural Information Processing Systems · 1988
Parallelizable optimization techniques are applied to the problem of learning in feedforward neural networks. In addition to having superior convergence properties, optimization techniques such as the Polak-Ribiere method are also significantly more efficient than the Backpropagation algorithm. These results are based on experiments performed on small boolean learning problems and the noisy real-valued learning problem of hand-written character recognition.