Exploring the Reduction of Configuration Spaces of Workflows
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-45275-8_3
Reference17 articles.
1. Abdulrahman, S.M., Brazdil, P., van Rijn, J.N., Vanschoren, J.: Speeding up algorithm selection using average ranking and active testing by introducing runtime. Mach. Learn. 107, 79–108 (2017). https://doi.org/10.1007/s10994-017-5687-8. Special Issue on Metalearning and Algorithm Selection
2. Abdulrahman, S.M., Brazdil, P., Zinon, M., Adamu, A.: Simplifying the algorithm selection using reduction of rankings of classification algorithms. In: ICSCA 2019 Proceedings of the 8th International Conference on Software and Computer Applications, Malaysia, pp. 140–148. ACM, New York (2019)
3. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012)
4. Brazdil, P., van Rijn, J., Soares, C., Vanschoren, J.: Metalearning approaches for algorithm selection I (exploiting rankings). In: Brazdil, P., van Rijn, J., Soares, C., Vanschoren, J. (eds.) Metalearning: Applications to Automated Machine Learning and Data Mining, pp. 19–37. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-67024-5_2
5. Brazdil, P., van Rijn, J., Soares, C., Vanschoren, J.: Setting-up configuration spaces and experiments. In: Brazdil, P., van Rijn, J., Soares, C., Vanschoren, J. (eds.) Metalearning: Applications to Automated Machine Learning and Data Mining, pp. 143–168. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-67024-5_8
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