A Simple Method for Solving the SVM Regularization Path for Semidefinite Kernels
By C. Sentelle, Georgios C. Anagnostopoulos, Michael Georgiopoulos. 2016
29 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition, Optimization & Training
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
- Jean-Baptiste Alayrac
- Simon Lacoste-Julien
- Anton Osokin
- Puneet Kumar Dokania
- Isabella Lukasewitz
- Peter Richtárik
- Martin Jaggi
- Ivan Laptev
In Computational Mechanics
- Jean-Baptiste Alayrac
- Anton Osokin
- Puneet Kumar Dokania
- Isabella Lukasewitz
- Simon Lacoste-Julien
- Peter Richtárik
- Fabian Pedregosa
- Sebastian Pokutta
In Computer Vision and Pattern Recognition
- Simon Lacoste-Julien
- Jean-Baptiste Alayrac
- Cordelia Schmid
- Ivan Laptev
- Luc Van Gool
- Puneet Kumar Dokania
- Andrew Zisserman
- Anton Osokin
In Optimization & Training
- Jean-Baptiste Alayrac
- Simon Lacoste-Julien
- Anton Osokin
- Puneet Kumar Dokania
- Isabella Lukasewitz
- Peter Richtárik
- Martin Jaggi
- Ivan Laptev
Citations in the corpus
- Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs (2016, methodology): “Although the exact path following methods for SVM are still developed (Sentelle et al., 2015), approximate methods might be more suited for practical use cases.”