A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention

@inproceedings{Guyon2016ABR,
  title={A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention},
  author={Isabelle Guyon and Imad Chaabane and Hugo Jair Escalante and Sergio Escalera and Damir Jajetic and James Robert Lloyd and N{\'u}ria Maci{\`a} and Bisakha Ray and Lukasz Romaszko and Mich{\`e}le Sebag and Alexander R. Statnikov and S{\'e}bastien Treguer and Evelyne Viegas},
  booktitle={AutoML@ICML},
  year={2016}
}
The ChaLearn AutoML Challenge team conducted a large scale evaluation of fully automatic, black-box learning machines for feature-based classification and regression problems. The test bed was composed of 30 data sets from a wide variety of application domains and ranged across different types of complexity. Over six rounds, participants succeeded in delivering AutoML software capable of being trained and tested without human intervention. Although improvements can still be made to close the… CONTINUE READING
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