Optimization of Gaussian process hyperparameters using Rprop.
Manuel BlumMartin A. RiedmillerPublished in: ESANN (2013)
Keyphrases
- gaussian process
- hyperparameters
- model selection
- cross validation
- gaussian processes
- grid search
- bayesian framework
- closed form
- bayesian inference
- support vector
- random sampling
- prior information
- covariance function
- noise level
- maximum a posteriori
- em algorithm
- gaussian process regression
- incremental learning
- posterior distribution
- maximum likelihood
- sample size
- variational bayes
- marginal likelihood
- bayesian methods
- approximate inference
- incomplete data
- parameter space
- regression model
- missing values
- latent variables
- prior knowledge
- parameter settings
- semi supervised
- expectation propagation
- machine learning
- feature selection
- active learning
- probabilistic model
- parameter estimation