Data-driven polynomial chaos expansion for machine learning regression.
Emiliano TorreStefano MarelliPaul EmbrechtsBruno SudretPublished in: J. Comput. Phys. (2019)
Keyphrases
- data driven
- machine learning
- model selection
- classification and regression problems
- regression model
- machine learning methods
- data mining
- machine learning algorithms
- neural network
- pattern recognition
- feature selection
- decision trees
- explanation based learning
- inductive learning
- statistical methods
- mercer kernels
- computational intelligence
- regression algorithm
- regression problems
- knowledge acquisition
- machine learning approaches
- gaussian processes
- learning tasks
- inductive logic programming
- knowledge base
- regression analysis
- feature space
- linear regression
- information extraction
- least squares
- learning algorithm
- artificial intelligence
- particle swarm optimization
- active learning
- text classification
- computer science
- polynomial regression
- regression function
- ridge regression
- knowledge representation
- low order
- learning machines
- computational biology
- computer vision
- learning systems
- support vector machine
- genetic algorithm
- kernel methods