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- W2767097468 abstract "In terms of (L_{1/2}) regularization, a novel feature selection method for a neural framework model has been developed in this paper. Due to the non-convex, non-smooth and non-Lipschitz characteristics of (L_{1/2}) regularizer, it is difficult to directly employ the gradient descent method in training multilayer perceptron neural networks. A smoothing technique has been considered to approximate the original (L_{1/2}) regularizer. The proposed method is a two-stage updating approach. First, a multilayer network model with smoothing (L_{1/2}) regularizer is trained to eliminate the unimportant features. Second, the compact model without regularization has been simulated until there is no improvements for the performance. The experiments demonstrate that the presented algorithm significantly reduces the redundant features while keeps a considerable model accuracy." @default.
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- W2767097468 date "2017-01-01" @default.
- W2767097468 modified "2023-09-27" @default.
- W2767097468 title "Feature Selection Using Smooth Gradient $$L_{1/2}$$ L 1 / 2 Regularization" @default.
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- W2767097468 doi "https://doi.org/10.1007/978-3-319-70093-9_17" @default.
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