Optimizing 0/1 Loss for Perceptrons by Random Coordinate Descent

By Ling Li, Hsuan-Tien Lin. 2007

21 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

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. Cynthia Rudin
2. Berk Ustun
3. Tan Minh Nguyen
4. Scott Sanner
5. Tong Wang
6. Lesia Semenova
7. Chudi Zhong
8. Zhi Chen

In Computational Theory and Mathematics

1. Berk Ustun
2. Scott Sanner
3. Cynthia Rudin
4. Allison Chang
5. Margo I. Seltzer
6. Ş. Ertekin
7. Tong Wang
8. Yining Wang

In Computer Vision and Pattern Recognition

1. Tan Minh Nguyen
2. Scott Sanner
3. Berk Ustun
4. Cynthia Rudin

Citations in the corpus

• Methods and Models for Interpretable Linear Classification (2014, background): “Linear classifiers that minimize surrogate loss functions, for instance, are not robust to outliers [67, 85].”
• Algorithms for Direct 0-1 Loss Optimization in Binary Classification (2013, background): “A margin mi(w) 0 indicates xi is misclassified, while mi(w) ≥ 0 indicates xi is correctly classified and mi represents the “margin of safety” by which the prediction for xi is correct (McAllester, 2007).”