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- W2147940826 abstract "The notion of approximator rejection is described, and applied to a neural network. For a real world classification problem the residual error is shown to decrease with the inverse exponential of the fraction of patterns rejected. The trade-off of “good” patterns rejected and “bad” patterns rejected is shown to increase approximately linearly with rejection rate. A compromise is therefore necessary between trade-off/rejection rate and residual error. A meta-level solution is proposed for removal of the residual error, through use of a modular system of parallel approximators." @default.
- W2147940826 created "2016-06-24" @default.
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- W2147940826 date "1993-01-01" @default.
- W2147940826 modified "2023-09-27" @default.
- W2147940826 title "Rejection of incorrect answers from a neural net classifier" @default.
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- W2147940826 doi "https://doi.org/10.1007/3-540-56798-4_196" @default.
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