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Random Search Plus: A More Effective Random Search For Machine Learning Hyperparameters Optimization, Bohan Li
Random Search Plus: A More Effective Random Search For Machine Learning Hyperparameters Optimization, Bohan Li
Masters Theses
Machine learning hyperparameter optimization has always been the key to improve model performance. There are many methods of hyperparameter optimization. The popular methods include grid search, random search, manual search, Bayesian optimization, population-based optimization, etc. Random search occupies less computations than the grid search, but at the same time there is a penalty for accuracy. However, this paper proposes a more effective random search method based on the traditional random search and hyperparameter space separation. This method is named random search plus. This thesis empirically proves that random search plus is more effective than random search. There are some case …
The Application Of Machine Learning Models In The Concussion Diagnosis Process, Sujit Subhash
The Application Of Machine Learning Models In The Concussion Diagnosis Process, Sujit Subhash
Masters Theses
“Concussions represent a growing health concern and are challenging to diagnose and manage. Roughly four million concussions are diagnosed every year in the United States. Although research into the application of advanced metrics such as neuroimages and blood biomarkers has shown promise, they are yet to be implemented at a clinical level due to cost and reliability concerns. Therefore, concussion diagnosis is still reliant on clinical evaluations of symptoms, balance, and neurocognitive status and function. The lack of a universal threshold on these assessments makes the diagnosis process entirely reliant on a physician’s interpretation of these assessment scores. This study …