• DocumentCode
    3661453
  • Title

    Design of the 2015 ChaLearn AutoML challenge

  • Author

    Isabelle Guyon;Kristin Bennett;Gavin Cawley;Hugo Jair Escalante;Sergio Escalera; Tin Kam Ho;Núria Macià;Bisakha Ray;Mehreen Saeed;Alexander Statnikov;Evelyne Viegas

  • Author_Institution
    ChaLearn, USA
  • fYear
    2015
  • fDate
    7/1/2015 12:00:00 AM
  • Firstpage
    1
  • Lastpage
    8
  • 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.
  • Keywords
    "Measurement","Reactive power"
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), 2015 International Joint Conference on
  • Electronic_ISBN
    2161-4407
  • Type

    conf

  • DOI
    10.1109/IJCNN.2015.7280767
  • Filename
    7280767