Random Machines Regression Approach: an ensemble support vector regression model with free kernel choice.
Anderson AraMateus MaiaSamuel MacêdoFrancisco LouzadaPublished in: CoRR (2020)
Keyphrases
- regression model
- support vector
- learning machines
- large margin classifiers
- kernel function
- kernel regression
- regression problems
- ensemble learning
- model selection
- support vector regression
- kernel methods
- regression methods
- feature selection
- regression analysis
- locally weighted
- regression method
- cross validation
- support vector machine
- gaussian process
- rbf kernel
- ensemble methods
- polynomial kernels
- logistic regression
- loss function
- reproducing kernel hilbert space
- multivariate regression
- generalization ability
- semi parametric
- linear regression model
- prediction model
- random forests
- generalized linear models
- linear model
- regression trees
- multi class
- logistic regression models
- input space
- svm classifier
- explanatory variables
- classification accuracy
- generalized linear
- survival analysis
- interval valued data
- target variable
- condition number
- multiple kernel learning
- binary classification
- hyperparameters
- learning algorithm
- independent variables
- training set
- multiple linear regression
- feature space
- training data
- support vectors
- gaussian processes
- linear discriminant analysis
- learning tasks
- support vector machine svm
- prediction intervals
- neural network