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- W2024325264 abstract "Most properties of learning and generalization in linear perceptrons can be derived from the average response function G. We present a method for calculating G using only simple matrix identities and partial differential equations. Using this method, we first rederive the known result for G in the thermodynamic limit of perceptrons of infinite size N, which has previously been calculated using replica and diagrammatic methods. We also show explicitly that the response function is self-averaging in the thermodynamic limit. Extensions of our method to more general learning scenarios with anisotropic teacher-space priors, input distributions, and weight-decay terms are discussed. Finally, finite-size effects are considered by calculating the O(1/N) correction to G. We verify the result by computer simulations and discuss the consequences for generalization and learning dynamics in linear perceptrons of finite size." @default.
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- W2024325264 date "1994-12-07" @default.
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- W2024325264 title "Finite-size effects in learning and generalization in linear perceptrons" @default.
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- W2024325264 doi "https://doi.org/10.1088/0305-4470/27/23/020" @default.
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