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Rprop

Known as: Resilient backpropagation, Resilient propagation 
Rprop, short for resilient backpropagation, is a learning heuristic for supervised learning in feedforward artificial neural networks. This is a… Expand
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Papers overview

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2013
2013
Gaussian processes are a powerful tool for non-parametric re- gression. Training can be realized by maximizing the likelihood of… Expand
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Highly Cited
2011
Highly Cited
2011
In this paper, we present a novel feature-based neural network (NN) approach for estimation of blood pressure (BP) from wrist… Expand
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2011
2011
Empirical models to correlate deformational modulus along with petrographic features which are intrinsic and inherent properties… Expand
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Highly Cited
2010
Highly Cited
2010
Artificial neural networks are applied in many situations. neuralnet is built to train multi-layer perceptrons in the context of… Expand
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2005
2005
Gradient-based optimization algorithms are the standard methods for adapting the weights of neural networks. The natural gradient… Expand
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Highly Cited
2003
Highly Cited
2003
Abstract The Rprop algorithm proposed by Riedmiller and Braun is one of the best performing first-order learning methods for… Expand
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Highly Cited
2000
Highly Cited
2000
The Rprop algorithm proposed by Riedmiller and Braun is one of the best performing first-order learning methods for neural… Expand
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Highly Cited
1994
Highly Cited
1994
A. General Description Rprop stands for 'Resilient backpropagation' and is a local adaptive learning scheme, performing… Expand
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Highly Cited
1994
Highly Cited
1994
Abstract Since the presentation of the backpropagation algorithm [1] a vast variety of improvements of the technique for training… Expand
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Highly Cited
1993
Highly Cited
1993
A learning algorithm for multilayer feedforward networks, RPROP (resilient propagation), is proposed. To overcome the inherent… Expand
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