health life sciences medicine
A major issue with many signal processing and machine learning algorithms is the lack of optimisation methods for determining the numerous hyper-parameters associated with the model as well as the knowledge of which hyper-parameters are relevant. These parameters are usually tunned by trial and error (manual search) or by grid search. In 2012 Bergstra et al. showed empirically that random search was performing similar or better than grid search while reducing the computational cost substantially.
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Random Search Toolbox was accessed on DATE from https://registry.opendata.aws/random-search.
arn:aws:s3:::physionet-open/random-search/us-east-1aws s3 ls --no-sign-request s3://physionet-open/random-search/