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• W2789122432 abstract "This paper introduces a novel measure-theoretic theory for machine learning that does not require statistical assumptions. Based on this theory, a new regularization method in deep learning is derived and shown to outperform previous methods in CIFAR-10, CIFAR-100, and SVHN. Moreover, the proposed theory provides a theoretical basis for a family of practically successful regularization methods in deep learning. We discuss several consequences of our results on one-shot learning, representation learning, deep learning, and curriculum learning. Unlike statistical learning theory, the proposed learning theory analyzes each problem instance individually via measure theory, rather than a set of problem instances via statistics. As a result, it provides different types of results and insights when compared to statistical learning theory." @default.
• W2789122432 created "2018-03-06" @default.
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• W2789122432 date "2018-02-21" @default.
• W2789122432 modified "2023-09-26" @default.
• W2789122432 title "Generalization in Machine Learning via Analytical Learning Theory" @default.
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• W2789122432 doi "https://doi.org/10.48550/arxiv.1802.07426" @default.
• W2789122432 hasPublicationYear "2018" @default.
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