Global ETD Search
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Showing 1 to 3 of 3 for “"Hyper-parameter optimization"”.
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Meta-level learning for the effective reduction of model search space.
… for less complex tasks that require thousands of parameters to learn. However, the state-of-the-art models, e.g. deep learning models, require well-tuned hyper-parameters to learn millions of parameters which demand specialized skills and numerous computationally expensive and time-consuming …
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Stacking Ensemble for auto_ml
… thesis begins by comparing auto ml with other hyper-parameter optimization techniques. auto ml is a fully autonomous framework that lessens the knowledge prerequisite to accomplish complicated machine learning tasks. The auto ml framework automatically selects the best features from a given …
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THERMODYNAMIC AND TRANSPORT PROPERTIES OF AVIATION TURBINE FUEL: PREDICTIVE APPROACHES USING ENTROPY SCALING GUIDED MACHINE LEARNING WITH EXPERIMENTAL VALIDATION
… intermediate step in the overall model. Simple hyper-parameter optimization techniques were developed to promote model stability, computational efficiency, and long-term repeatability of the approach. Additionally, a model for predicting temperature dependent isobaric specific heats of liquids …