Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge

By Isabelle M Guyon, Amir Saffari, Gideon Dror, Gavin C. Cawley. 2008

34 citations recorded by the source corpus.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Frank Hutter
2. Marco F. Huber
3. Marc-André Zöller
4. Marius Thomas Lindauer
5. Katharina Eggensperger
6. Stefan Falkner
7. Matthias Feurer
8. Aaron Klein

Citations in the corpus

• Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “Testing this approach on various data sets, the potential of this domain-agnostic method was proven (Guyon et al., 2008).”
• Survey on Automated Machine Learning (2019, positive): “Testing this approach on various data sets, the potential of this domain-agnostic method was proven (Guyon et al., 2008).”
• Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning (2020, methodology): “It used a fixed-length representation of the pipeline and contained feature selection, feature processing, classification and postprocessing implemented in the CLOP package9 and was developed for the IJCNN 2007 agnostic learning vs. prior knowledge challenge (Guyon et al., 2008).”