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- W3128710458 abstract "As a deep algorithm of non-neural network structure, deep forest regression (DFR) can be used to build soft measuring models of difficult-to-measure key parameters. However, as a kind of deep learning, the optimization of hyperparameters has become an inevitable problem in DFR. To solve above problem, an improved dynamic state transition algorithm (DSTA) is used to optimize the hyper-parameters of the model. To achieved more accurate optimization process, the error change rate is used to fine-tuning the state factor during the iteration process, which is further improved with gradient-based refinement. Finally, simulation experiments are performed on the benchmark data set, and satisfactory simulation results show the effectiveness of the proposed approach." @default.
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- W3128710458 date "2020-11-06" @default.
- W3128710458 modified "2023-09-27" @default.
- W3128710458 title "Deep Forest Regression Based on Dynamic State Transition Optimization Algorithm" @default.
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- W3128710458 doi "https://doi.org/10.1109/cac51589.2020.9327803" @default.
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