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Trends and Applications in Constructive Approximation
Gradient-based optimization algorithms are the standard methods for adapting the weights of neural networks. The natural gradient gives the steepest descent direction based on a non-Euclidean, from a theoretical point of view more appropriate metric in the weight space. While the natural gradient has already proven to be advantageous for online learning, we explore its benefits for batch learning: We empirically compare Rprop (resilient backpropagation), one of the best performing first-orderdoi:10.1007/3-7643-7356-3_19 fatcat:34r54yj5mfgcvdn2ozsoujhp5q