Information Processing Letters
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Fast Convergent Generalized Back-Propagation Algorithm with Constant Learning Rate
Neural Processing Letters
A fast, compact approximation of the exponential function
Neural Computation
Modeling with constructive backpropagation
Neural Networks
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
IEEE Transactions on Neural Networks
Asymptotic statistical theory of overtraining and cross-validation
IEEE Transactions on Neural Networks
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
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Although neural networks have many appealing properties, yet there is neither a systematic way how to set up the topology of a neural network nor how to determine its various learning parameters. Thus an expert is needed for fine tuning. If neural network applications should not be realisable only for publications but in real life, fine tuning must become unnecessary. In the present paper an approach is demonstrated fulfilling this demand. Moreover referring to six medical classification and approximation problems of the PROBEN1 benchmark collection this approach will be shown even to outperform fine tuned networks.