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- W3007872010 abstract "A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''. General loss functions are considered under this unified framework with specific examples presented for classification, regression, and learning to rank. A fully corrective step is incorporated to remedy the pitfall of greedy function approximation of classic gradient boosting decision tree. The proposed model rendered outperforming results against state-of-the-art boosting methods in all three tasks on multiple datasets. An ablation study is performed to shed light on the effect of each model components and model hyperparameters." @default.
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- W3007872010 date "2020-02-19" @default.
- W3007872010 modified "2023-09-27" @default.
- W3007872010 title "Gradient Boosting Neural Networks: GrowNet" @default.
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