Regression random machines: An ensemble support vector regression model with free kernel choice.
Anderson AraMateus MaiaFrancisco LouzadaSamuel MacêdoPublished in: Expert Syst. Appl. (2022)
Keyphrases
- regression model
- support vector
- learning machines
- large margin classifiers
- kernel function
- kernel regression
- regression problems
- regression methods
- model selection
- kernel methods
- support vector regression
- regression method
- locally weighted
- ensemble learning
- regression analysis
- multivariate regression
- rbf kernel
- cross validation
- logistic regression
- prediction model
- linear regression model
- survival analysis
- condition number
- feature selection
- gaussian process
- neural network
- linear model
- generalization ability
- generalized linear models
- radial basis function
- multiple linear regression
- loss function
- regression trees
- support vector machine
- ensemble methods
- polynomial kernels
- classification accuracy
- explanatory variables
- response variable
- learning algorithm
- reproducing kernel hilbert space
- independent variables
- random forests
- hyperparameters
- svm classifier
- multi class
- logistic regression models
- feature space
- ridge regression
- kernel matrix
- binary classification
- learning problems
- high dimensional
- interval valued data