SVM vs regularized least squares classification

By Peng Zhang, Jing Peng. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004

330 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b066702a859165fb4c42d35456154aff74da3bba

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. Mikhail Belkin
2. Siyuan Ma
3. Soumik Mandal
4. Vitaly Feldman
5. Chaoyue Liu
6. Deng Cai
7. Amit Singer
8. Xiaofei He

In Computational Mechanics

1. Mikhail Belkin
2. Soumik Mandal
3. Siyuan Ma
4. Chaoyue Liu
5. Partha P. Mitra
6. Vitaly Feldman
7. Daniel J. Hsu
8. Raef Bassily

In Computer Vision and Pattern Recognition

1. Mikhail Belkin
2. Siyuan Ma
3. Soumik Mandal
4. Dacheng Tao
5. Luc Van Gool
6. Deng Cai
7. Ming-Hsuan Yang
8. Shuicheng Yan

Citations in the corpus

• To understand deep learning we need to understand kernel learning (2018, methodology): “For example, kernel SVM performs very similarly to kernel least square regression [ZP04].”