IEEE International Conference on Neural Networks · 2002 · 3.9K citations · 6 references
ComputingComputer ScienceMachine LearningNeural EngineeringOptimizationGradient-based OptimizationLearning In Neural SystemsLoss FunctionNeural NetworksOptimization For Machine LearningPattern RecognitionBackpropagationFeedforward Neural NetworksGradient DescentMultilayer NetworksMultilayer PerceptronOptimization AlgorithmsTransparent Adaptation
A learning algorithm for multilayer feedforward networks, RPROP (resilient propagation), is proposed. To overcome the inherent disadvantages of pure gradient-descent, RPROP performs a local adaptation of the weight-updates according to the behavior of the error function. Contrary to other adaptive techniques, the effect of the RPROP adaptation process is not blurred by the unforeseeable influence of the size of the derivative, but only dependent on the temporal behavior of its sign. This leads to an efficient and transparent adaptation process. The capabilities of RPROP are shown in comparison to other adaptive techniques.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Increased rates of convergence through learning rate adaptation
Robert A. Jacobs · Neural Networks · 1988 · 1.8K citations
Learning to Tell Two Spirals Apart
Kevin Lang · 1988 · 288 citations · Full text
Learning to tell two spirals apart
Kevin Lang · Medical Entomology and Zoology · 1988 · 169 citations