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- W1754345803 abstract "A probabilistic radial basis function PRBF network is an effective non-linear classifier. However, similar to most other neural network models it is non-transparent, which makes its predictions difficult to interpret. In this paper we show how a one-variable-at-a-time and an all-subsets explanation method can be modified for an equivalent and more efficient use with PRBF network classifiers. We use several artificial and real-life data sets to demonstrate the usefulness of the visualizations and explanations of the PRBF network classifier." @default.
- W1754345803 created "2016-06-24" @default.
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- W1754345803 date "2013-09-26" @default.
- W1754345803 modified "2023-09-27" @default.
- W1754345803 title "Efficiently explaining the predictions of a probabilistic radial basis function classification network" @default.
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- W1754345803 doi "https://doi.org/10.3233/ida-130607" @default.
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