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GENERATING HYPER-PARAMETERS FOR MACHINE LEARNING MODELS USING MODIFIED BAYESIAN OPTIMIZATION BASED ON ACCURACY AND TRAINING EFFICIENCY
GENERATING HYPER-PARAMETERS FOR MACHINE LEARNING MODELS USING MODIFIED BAYESIAN OPTIMIZATION BASED ON ACCURACY AND TRAINING EFFICIENCY
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机译:基于精度和培训效率,使用改进的贝叶斯优化产生机器学习模型的超参数
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摘要
The present disclosure relates to systems, methods, and non-transitory computer readable media for selecting hyper-parameter sets by utilizing a modified Bayesian optimization approach based on a combination of accuracy and training efficiency metrics of a machine learning model. For example, the disclosed systems can fit accuracy regression and efficiency regression models to observed metrics associated with hyper-parameter sets of a machine learning model. The disclosed systems can also implement a trade-off acquisition function that implements an accuracy-training efficiency balance metric to explore the hyper-parameter feature space and select hyper-parameters for training the machine learning model considering a balance between accuracy and training efficiency.
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