Kernel and Acquisition Function Setup for Bayesian Optimization of Gradient Boosting Hyperparameters.
Andrzej SzwabePublished in: ACIIDS (1) (2018)
Keyphrases
- hyperparameters
- gaussian processes
- gradient boosting
- posterior distribution
- bayesian inference
- support vector
- model selection
- cross validation
- gaussian process
- maximum likelihood
- bayesian framework
- closed form
- prior information
- random sampling
- sample size
- loss function
- maximum a posteriori
- posterior probability
- noise level
- em algorithm
- decision trees
- incremental learning
- regression problems
- kernel methods
- kernel function
- parameter settings
- feature extraction
- probabilistic model
- active learning
- feature vectors
- bayesian networks
- training data