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 [BibTeX] [Marc21]
A Boolean Approach to Construct Neural Networks for Non-Boolean Problems
Type of publication: Conference paper
Citation: Thimm-96.3
Booktitle: Proceedings of the 8th IEEE International Conference on Tools with Artificial Intelligence
Year: 1996
Month: 11
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.
Userfields: ipdhtml={https://www.idiap.ch/nn-papers/long_ICTAI/growing.html}, ipdmembership={learning},
Keywords: backpropagation neural networks, construction of networks, generalization, high order perceptrons, optimality criteria, pruning
Projects Idiap
Authors Thimm, Georg
Fiesler, Emile
Added by: [UNK]
Total mark: 0