Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems
By Sepp Hochreiter, Michael C. Mozer, Klaus Obermayer. 2002
24 citations recorded by the source corpus.
Subfields: Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Representation & Self-Supervised Learning
- Thomas Unterthiner
- Sepp Hochreiter
- Günter Klambauer
- Stephan K. Chalup
- Klaus Obermayer
- Andreas Mitschele
- Bernhard Nessler
- J. Urgen Schmidhuber
Citations in the corpus
- Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields (2017, background): “Another related work is the Potential Support Vector Machine (PSVM), which minimizes Coulomb potential differences [21, 22].”
- Kernel Methods in Finance (2008, background): “with the relatively unknown Coulomb kernel (1 + ‖xi − xj‖2/E)−δ (Hochreiter et al., 2003), which represents a localised kernel function similar to RBF kernels, indicated better classification performance based on a lower expected out-of-sample error than linear, polynomial, sigmoid, and RBF kernels.”
- Optimal kernels for unsupervised learning (2005, background): “They are widely used in computational physics, but have recently also been introduced as a useful kernel for support vector learning [4].”