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Your query term was 'number = 95-20'1 report found
- ÖFAI-TR-95-20 (
116kB g-zipped PostScript file)A generalized view on learning in feedforward neural networks
- Georg Dorffner
- In this paper we introduce a generalized view on feedforward neural networks. In this view, well-known network types like multilayer perceptrons and radial basis function networks are just a few of many possibilities in a virtual space of neural network types, spanned by the three dimensions propagation rule, transfer function, and learning rule. We list several examples of other combinations of values along these dimensions and discuss the advantages of such a view. The goal of depicting neural networks this way is to arrive at strategies to find optimal neural network solutions for given data sets, aided by statistical data analysis to identify the best method.