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• W2988893955 abstract "Abstract Researchers and educators have long wrestled with the question of how best to teach their clients be they humans, non-human animals or machines. Here, we examine the role of a single variable, the difficulty of training, on the rate of learning. In many situations we find that there is a sweet spot in which training is neither too easy nor too hard, and where learning progresses most quickly. We derive conditions for this sweet spot for a broad class of learning algorithms in the context of binary classification tasks. For all of these stochastic gradient-descent based learning algorithms, we find that the optimal error rate for training is around 15.87% or, conversely, that the optimal training accuracy is about 85%. We demonstrate the efficacy of this ‘Eighty Five Percent Rule’ for artificial neural networks used in AI and biologically plausible neural networks thought to describe animal learning." @default.
• W2988893955 created "2019-11-22" @default.
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• W2988893955 date "2019-11-05" @default.
• W2988893955 modified "2023-10-16" @default.
• W2988893955 title "The Eighty Five Percent Rule for optimal learning" @default.
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• W2988893955 doi "https://doi.org/10.1038/s41467-019-12552-4" @default.
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• W2988893955 hasPublicationYear "2019" @default.
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