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- W2015140204 abstract "Learning from examples is the process of taking input-output examples of an unknown function ƒ and infering an implementation of ƒ. Learning from hints allows for general information about ƒ to be used instead of just input-output examples. We introduce a method for incorporating any invariance hint about ƒ in any descent method for learning from examples. We also show that learning in a neural network remains NP-complete with a certain, biologically plausible, hint about the network. We discuss the information value and the complexity value of hibts." @default.
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- W2015140204 date "1990-06-01" @default.
- W2015140204 modified "2023-10-03" @default.
- W2015140204 title "Learning from hints in neural networks" @default.
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- W2015140204 doi "https://doi.org/10.1016/0885-064x(90)90006-y" @default.
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