Expected kernel for missing features in support vector machines
By Hyrum S. Anderson, Maya R. Gupta. 2011
13 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical Learning Theory
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
- Krikamol Muandet
- Bharath K. Sriperumbudur
- Bernhard Schölkopf
- Kenji Fukumizu
- Arthur Gretton
- David Lopez-Paz
- Dino Sejdinovic
- Jan Peters
In Computer Vision and Pattern Recognition
- Bharath K. Sriperumbudur
- Krikamol Muandet
- Kenji Fukumizu
- Bernhard Schölkopf
- Francesco Dinuzzo
- David Lopez-Paz
- Luc Van Gool
- Arthur Gretton
In Statistical Learning Theory
- Bharath K. Sriperumbudur
- Kenji Fukumizu
- Bernhard Schölkopf
- Krikamol Muandet
- Arthur Gretton
- Dino Sejdinovic
- Le Song
- David Lopez-Paz
Citations in the corpus
- Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “Anderson and Gupta (2011) showed the equivalence between SVMs using expected kernels and SOCP when τi = 0. When τi > 0, the mean and covariance of missing kernel entries have to be estimated explicitly, making the SOCP more involved for nonlinear kernels. Although achieving comparable performance to”
- Learning from Distributions via Support Measure Machines (2012, methodology): “Anderson & Gupta (2011) showed the equivalence between SVMs using expected kernels and SOCP when τi = 0.”