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- W2769350044 abstract "We develop an approximate formula for evaluating a cross-validation estimator of predictive likelihood for multinomial logistic regression regularized by an $ell_1$-norm. This allows us to avoid repeated optimizations required for literally conducting cross-validation; hence, the computational time can be significantly reduced. The formula is derived through a perturbative approach employing the largeness of the data size and the model dimensionality. An extension to the elastic net regularization is also addressed. The usefulness of the approximate formula is demonstrated on simulated data and the ISOLET dataset from the UCI machine learning repository." @default.
- W2769350044 created "2017-12-04" @default.
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- W2769350044 date "2017-11-15" @default.
- W2769350044 modified "2023-09-27" @default.
- W2769350044 title "Accelerating Cross-Validation in Multinomial Logistic Regression with $ell_1$-Regularization" @default.
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