Regression kernel for prognostics with support vector machines.
Josey MathewMing LuoChee Khiang PangPublished in: ETFA (2017)
Keyphrases
- kernel ridge regression
- reproducing kernel hilbert space
- sparse kernel
- gaussian processes
- kernel regression
- classification and regression problems
- kernel function
- regression model
- support vector
- regression method
- gaussian process regression
- support vector regression
- principal component regression
- relevance vector machine
- linear regression
- regression algorithm
- regression problems
- regression methods
- kernel partial least squares
- feature space
- input space
- partial least squares
- gradient boosting
- covariance function
- kernel methods
- regression analysis
- kernel machines
- simple linear
- kernel matrix
- fault detection
- component analysis
- mercer kernels
- loss function
- kernel space
- polynomial regression
- model selection
- hilbert schmidt independence criterion
- support vector machine
- feature set
- similarity measure
- kernel matrices
- regression function
- ridge regression
- gaussian kernels
- positive definite
- graph kernels