What size net gives valid generalization?
Neural Computation
Creating artificial neural networks that generalize
Neural Networks
Neural Computation
Journal of Complexity
The nature of statistical learning theory
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Neural Networks
Neural networks for pattern recognition
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Neurosmithing: improving neural network learning
The handbook of brain theory and neural networks
No free lunch for early stopping
Neural Computation
Rule extraction by successive regularization
Neural Networks
Information Retrieval
Machine Learning
Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
Evolving efficient learning algorithms for binary mappings
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Data smoothing regularization, multi-sets-learning, and problem solving strategies
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
The “weight smoothing” regularization of MLP for Jacobian stabilization
IEEE Transactions on Neural Networks
Improving generalization performance using double backpropagation
IEEE Transactions on Neural Networks
Curvature-driven smoothing: a learning algorithm for feedforward networks
IEEE Transactions on Neural Networks
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In standard BP-networks, hidden neuron outputs are usually spread over the whole interval (0,1). In this paper, we propose an efficient framework to enforce a transparent internal knowledge representation in BP-networks during training. We want the formed internal representations to differ as much as possible for different outputs. At the same time, the hidden neuron outputs will be forced to group around three possible values, namely 1, 0 and 0.5. We will call such an internal representation unambiguous and condensed. The performance of BP-networks with enforced internal representations will be examined in a case study devoted to semantic image classification.