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524
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Regularization Theory and Neural Networks Architectures
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Fundamentals of Artificial Neural Networks
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67
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30 years of adaptive neural networks: Perceptron, Madaline, and backpropagation
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56
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Regression modeling in back-propagation and projection pursuit learning
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42
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Statistical theory of learning curves under entropic loss criterion
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38
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On the relationship between generalization error, hypothesis complexity, and sample complexity for radial basis functions
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35
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Structural risk minimization for character recognition
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26
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Universal Approximation Using Feedforward Networks with Nonsigmoid Hidden Layer Activation Functions
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17
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Generalization and PAC learning: Some new results for the class of generalized single layer networks
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13
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A universal theorem on learning curves
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12
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A simplified gradient algorithm for IIR synapse multilayer perceptrons
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12
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A feedforward neural network with function shape autotuning
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11
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Artificial Neural Networks with Adaptive Polynomial Activation Function
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11
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Similarities of error regularization, sigmoid gain scaling, target smoothing, and training with jitter
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9
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Multilayer Neural Networks with Adaptive Spline-Based Activation Functions
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9
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Neural Networks with Digital LUT Activation Function
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4
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Neural Networks with Adaptive Spline Activation Function
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2
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Fast spline neural networks for image compression
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1
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Architecture selection startegies for neural networks: application to corporate bond rating prediction
– Moody
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