%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Thimm-96.3,
         author = {Thimm, Georg and Fiesler, Emile},
       keywords = {backpropagation neural networks, construction of networks, generalization, high order perceptrons, optimality criteria, pruning},
       projects = {Idiap},
          month = {11},
          title = {A Boolean Approach to Construct Neural Networks for Non-Boolean Problems},
      booktitle = {Proceedings of the 8th IEEE International Conference on Tools with Artificial Intelligence},
           year = {1996},
   organization = {IEEE},
       abstract = {A neural network construction method for problems specified for data sets with in- and/or output values in the continuous or discrete domain is described and evaluated. This approach is based on a Boolean approximation of the data set and is generic for various neural network architectures. The construction method takes advantage of a construction method for Boolean problems without increasing the dimensions of the in- or output vectors, which is a strong advantage over approaches which work on a binarized version of the data set with an increased number of in- and output elements. Further, the networks are pruned in a second phase in order to obtain very small networks.},
ipdhtml={https://www.idiap.ch/nn-papers/long_ICTAI/growing.html},
ipdmembership={learning},
}