| Authors | R. Borgli, H. Borgli, P. Halvorsen, M. Riegler, and H. K. Stensland |
| Title | Automatic Hyperparameter Optimization in Keras for the MediaEval 2018 Medico Multimedia Task |
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Status | Published |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2018 |
| Conference Name | Working Notes Proceedings of the MediaEval 2018 Workshop |
| Publisher | CEUR Workshop Proceedings (CEUR-WS.org) |
| Keywords | automatic hyperparameter optimization, Bayesian optimization, CNN, convolutional neural networks, dataset manipulation, gpyopt, hyperparameter optimization, keras, saga, tensorflow, Transfer Learning |
| Abstract | This paper details the approach to the MediaEval 2018 Medico Multimedia Task made by the Rune team. The decided upon approach uses a work-in-progress hyperparameter optimization system called Saga. Saga is a system for creating the best hyperparameter finding in Keras, a popular machine learning framework, using Bayesian optimization and transfer learning. In addition to optimizing the Keras classifier configuration, we try manipulating the dataset by adding extra images in a class lacking in images and splitting a commonly misclassified class into two classes. |
| Citation Key | runeMedico2018 |
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