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- W65708977 abstract "We consider Extreme Learning Machine model for accurate regression estimation and related problem of selecting appropriate number of neurons for model. Selection strategies that choose the best model from a set of candidate network structures neglect issues of model selection uncertainty. To alleviate problem, we propose to remove this selection phase with a combination layer that takes into account all considered models. The proposed method in this paper is Extreme Learning Machine(Jackknife Model Averaging), where Jackknife Model Averaging is a combination method based on leave-one-out residuals of linear models. The combination approach is shown to have better predictive performance on several real-world data sets." @default.
- W65708977 created "2016-06-24" @default.
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- W65708977 date "2013-01-01" @default.
- W65708977 modified "2023-09-24" @default.
- W65708977 title "Extending Extreme Learning Machine with Combination Layer" @default.
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- W65708977 doi "https://doi.org/10.1007/978-3-642-38679-4_41" @default.
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