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- W2514898078 abstract "Logistic regression is a statistical model widely used for solving classification problems. Maximum likelihood is used train the model parameters. When data from two classes is linearly separable, maximum likelihood is ill-posed. To address this problem as well as to handle over-fitting issues, regularization is commonly considered. A regularization coefficient is used to control the tradeoff between model complexity and data fit and cross-validation is applied to determine the coefficient. In this paper, we develop a regularization framework for logistic regression using Jeffreys prior, which is free of any tuning parameters. Our experiments show that the proposed regularization outperforms other well-known regularization approaches." @default.
- W2514898078 created "2016-09-16" @default.
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- W2514898078 date "2016-06-01" @default.
- W2514898078 modified "2023-09-27" @default.
- W2514898078 title "Jeffreys prior regularization for logistic regression" @default.
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- W2514898078 doi "https://doi.org/10.1109/ssp.2016.7551820" @default.
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