Boosting Classifiers with Tightened L0-Relaxation Penalties

By Noam Goldberg, Jonathan Eckstein. 2010

10 citations recorded by the source corpus.

How researchers see this paper

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

1. Cynthia Rudin
2. Berk Ustun
3. Tong Wang
4. Marc E. Pfetsch
5. Sebastian Pokutta
6. Nadia Burkart
7. Ş. Ertekin
8. Zhi Chen

Citations in the corpus

• Supersparse linear integer models for optimized medical scoring systems (2015, background): “Similarly, MIP formulations that penalize the `0-norm for feature selection were proposed in Goldberg and Eckstein (2010, 2012) and Nguyen and Franke (2012).”
• IPBoost - Non-Convex Boosting via Integer Programming (2020, methodology): “These cuts dominate the ones in (Goldberg & Eckstein, 2010), but one needs to solve an LP for each vertex/cut.”