Two smooth support vector machines for \(\varepsilon \) -insensitive regression.
Weizhe GuWei-Po ChenChun-Hsu KoYuh-Jye LeeJein-Shan ChenPublished in: Comput. Optim. Appl. (2018)
Keyphrases
- support vector
- learning machines
- large margin classifiers
- vc dimension
- regression problems
- support vector regression
- kernel function
- feature selection
- model selection
- logistic regression
- sample size
- support vector machine
- loss function
- kernel methods
- maximum margin
- cross validation
- structural risk minimization
- hyperplane
- binary classification
- training examples
- svm classifier
- generalization ability
- training data
- polynomial kernels
- soft margin
- ridge regression
- generalization bounds
- regression model
- linear regression
- regression methods
- support vectors
- sample complexity
- generalization error
- input space
- data points
- machine learning
- mercer kernels