Tools
Tools
Guyon, Isabelle, Cawley, Gavin 
ORCID: https://orcid.org/0000-0002-4118-9095, Bennett, Kristin, Jair Escalente, Hugo, Escalera, Sergio, Ho, Tin Kam, Macia, Nuria, Ray, Bisakha, Saeed, Mehreen, Statnikov, Alexander and Viegas, Evelyne
(2015)
Design of the 2015 ChaLearn AutoML challenge.
In:
Proceedings of International Joint Conference on Neural Networks (IJCNN).
The Institute of Electrical and Electronics Engineers (IEEE).
Abstract
ChaLearn is organizing the Automatic Machine Learning (AutoML) contest for IJCNN 2015, which challenges participants to solve classification and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the prizes can be won by submitting prediction results only. Datasets of progressively increasing difficulty are introduced throughout the six rounds of the challenge. (Participants can enter the competition in any round.) The rounds alternate phases in which learners are tested on datasets participants have not seen, and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance. This challenge will push the state of the art in fully automatic machine learning on a wide range of real-world problems. The platform will remain available beyond the termination of the challenge.
| Item Type: | Book Section |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences |
| Depositing User: | Pure Connector |
| Date Deposited: | 02 Feb 2016 13:13 |
| Last Modified: | 20 Apr 2023 01:09 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/56932 |
| DOI: | 10.1109/IJCNN.2015.7280767 |
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