Kernel Approximation: From Regression to Interpolation.
Lulu KangV. Roshan JosephPublished in: SIAM/ASA J. Uncertain. Quantification (2016)
Keyphrases
- kernel ridge regression
- covariance function
- reproducing kernel hilbert space
- gaussian processes
- gaussian process regression
- kernel regression
- series expansion
- classification and regression problems
- support vector
- sparse kernel
- gaussian convolution
- regression model
- cubic convolution
- kernel function
- image interpolation
- polynomial kernels
- regression method
- mercer kernels
- relevance vector machine
- gaussian kernels
- regression algorithm
- feature space
- approximation error
- approximation algorithms
- support vector regression
- kernel partial least squares
- regression methods
- hilbert schmidt independence criterion
- model selection
- closed form
- error bounds
- linear regression
- ridge regression
- linear interpolation
- partial least squares
- gaussian kernel
- gaussian process
- regression analysis
- kernel methods
- regression problems
- loss function
- artificial neural networks
- principal component regression