Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum.
Tin Sum ChengAurélien LucchiAnastasis KratsiosDavid BeliusPublished in: CoRR (2024)
Keyphrases
- kernel ridge regression
- reproducing kernel hilbert space
- classification and regression problems
- sparse kernel
- kernel regression
- gaussian processes
- support vector
- regression model
- regression method
- gaussian process regression
- kernel methods
- cross validation
- kernel function
- regression problems
- principal component regression
- linear regression
- model selection
- decision trees
- covariance function
- relevance vector machine
- kernel partial least squares
- machine learning
- mercer kernels
- locally weighted
- support vector regression
- gradient boosting
- kernel matrix
- hilbert schmidt independence criterion
- conditional density estimation
- partial least squares
- feature space
- least squares
- kernel machines
- ridge regression
- regression algorithm
- genetic programming
- loss function
- euclidean space
- positive definite
- polynomial kernels
- multiple kernel learning
- training data
- convolution kernel