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
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. We developed a tool called ACMD (Approximation and Classification of Medical Data) that is demonstrated to fulfil 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.