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• W2090921907 abstract "The effect of the structure of the input distribution on the complexity of learning a pattern classification task is investigated. Using statistical mechanics, we study the performance of a winner-take-all machine at learning to classify points generated by a mixture of K Gaussian distributions (``clusters'') in ${mathit{R}}^{mathit{N}}$ with intercluster distance u (relative to the cluster width). In the separation limit uensuremath{gg}1, the number of examples required for learning scales as ${mathit{NKu}}^{mathrm{ensuremath{-}}mathit{p}}$, where the exponent p is 2 for zero-temperature Gibbs learning and 4 for the Hebb rule." @default.
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• W2090921907 date "1993-05-17" @default.
• W2090921907 modified "2023-09-27" @default.
• W2090921907 title "Scaling laws in learning of classification tasks" @default.
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• W2090921907 doi "https://doi.org/10.1103/physrevlett.70.3167" @default.
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