Extending Gaussian process emulation using cluster analysis and artificial neural networks to fit big training sets.
Wim De MulderBernhard RengsGeert MolenberghsThomas FentGeert VerbekePublished in: J. Simulation (2019)
Keyphrases
- cluster analysis
- gaussian process
- artificial neural networks
- training set
- gaussian processes
- regression model
- model selection
- hyperparameters
- approximate inference
- k means
- clustering algorithm
- neural network
- supervised learning
- unsupervised learning
- active learning
- bayesian framework
- semi supervised
- clustering method
- latent variables
- factor analysis
- data mining
- data analysis
- data mining techniques
- hierarchical latent class models
- random sampling
- genetic algorithm ga
- probability distribution
- feature space
- prior knowledge
- decision trees
- real world
- knowledge discovery
- message passing
- support vector
- training data
- computer vision