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- W2937224478 abstract "The most frequently used deep learning models are deep neural networks.Although they have been successfully applied to various problems, they re-quire large training sets and careful tuning of parameters. An alternativeto deep neural networks is the deep forest model, which we independentlyimplemented to verify the replicability of results in (Zhou and Feng, 2017).We test if the accuracy of deep forest can be improved by including ran-dom subspace forests or by using stacking to combine predictions of cascadeforest's last layer.We evaluate the original implementation and our improvements on fivedata sets. The algorithm with added stacking achieves equal or better resultson all five data sets, whereas the addition of random subspace forests bringsworse results on three data sets and better results on two data sets." @default.
- W2937224478 created "2019-04-25" @default.
- W2937224478 creator A5020887390 @default.
- W2937224478 date "2018-09-14" @default.
- W2937224478 modified "2023-09-27" @default.
- W2937224478 title "Improving deep random forests" @default.
- W2937224478 hasPublicationYear "2018" @default.
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