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• W2810818231 abstract "In this paper, a novel, automated process for constructing and initializing deep feedforward neural networks based on decision trees is presented. The proposed algorithm maps a collection of decision trees trained on the data into a collection of initialized neural networks with the structures of the networks determined by the structures of the trees. The tree-informed initialization acts as a warm-start to the neural network training process, resulting in efficiently trained, accurate networks. These models, referred to as deep jointly informed neural networks (DJINN), demonstrate high predictive performance for a variety of regression and classification data sets and display comparable performance to Bayesian hyperparameter optimization at a lower computational cost. By combining the user-friendly features of decision tree models with the flexibility and scalability of deep neural networks, DJINN is an attractive algorithm for training predictive models on a wide range of complex data sets." @default.
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• W2810818231 date "2019-05-01" @default.
• W2810818231 modified "2023-10-03" @default.
• W2810818231 title "Deep Neural Network Initialization With Decision Trees" @default.
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• W2810818231 doi "https://doi.org/10.1109/tnnls.2018.2869694" @default.
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