Uncertainty Bounds for Kernel-based Regression: a Bayesian SPS Approach.
Algo CarèGianluigi PillonettoMarco C. CampiPublished in: MLSP (2018)
Keyphrases
- sparse bayesian learning
- decision theory
- kernel ridge regression
- dempster shafer
- pac bayesian
- sparse kernel
- gaussian processes
- support vector
- regression model
- upper bound
- lower bound
- relevance vector machine
- support vector machine
- uncertain data
- lower and upper bounds
- belief functions
- model selection
- learning machines
- worst case
- bayesian learning
- error bounds
- kernel methods
- maximum likelihood
- online learning
- simple linear
- regression method
- linear regression
- expected utility
- regression analysis
- bayesian inference
- posterior probability
- machine learning
- dirichlet process mixture
- regression problems
- upper and lower bounds
- vc dimension
- conditional probabilities
- multi class
- learning algorithm