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- W1980055363 abstract "The validation of input-data samples is a very important issue for neural-net applications if high precision is required. The aim is to make sure that a neural net properly trained on one data sample gives reliable results for another. Using the search for the standard Higgs boson at LEP-200 as an example, the effect of systematic changes in the input data is studied in detail for feed-forward nets. Several methods for recognizing significant differences between the training-data sample and a test-data sample are presented." @default.
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- W1980055363 date "1995-02-01" @default.
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- W1980055363 title "Validation of input data for trained neural-nets" @default.
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- W1980055363 doi "https://doi.org/10.1016/0010-4655(94)00142-o" @default.
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