Neural Computation · 1996 · 25 citations · 7 references
ComputingComputer ScienceArtificial IntelligenceMachine LearningOptimizationError-based LearningNeural Circuit ModelingNeural CircuitsNeural ModelingNeural NetworksPattern RecognitionBackpropagationError SurfacesGradient DescentMultilayer PerceptronNeural Network ModelingSaddle Point OptimizationStationary PointsStationary States
The artificial neural network with one hidden unit and the input units connected to the output unit is considered. It is proven that the error surface of this network for the patterns of the XOR problem has minimum values with zero error and that all other stationary points of the error surface are saddlepoints. Also, the volume of the regions in weight space with saddlepoints is zero, hence training this network on the four patterns of the XOR problem using, e.g., backpropagation with momentum, the correct solution with error zero will be reached in the limit with probability one.
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Introduction to the theory of neural computation
Neural Networks · 1994 · 6.5K citations
Large Automatic Learning, Rule Extraction, and Generalization.
John S. Denker, Daniel Schwartz, Ben S. Wittner et al. · Complex Systems · 1987 · 249 citations
Daniel Schwartz, Vijay K. Samalam, Sara A. Solla et al. · Neural Computation · 1990 · 73 citations