Leave-One-Out Bounds for Support Vector Regression Model Selection.
Ming-Wei ChangChih-Jen LinPublished in: Neural Comput. (2005)
Keyphrases
- model selection
- support vector regression
- cross validation
- regression model
- generalization bounds
- learning machines
- hyperparameters
- sample size
- variable selection
- hybrid model
- gaussian process
- upper bound
- feature selection
- parameter estimation
- lower bound
- support vector classification
- support vector
- machine learning
- mixture model
- motion segmentation
- support vector machine svm
- information criterion
- meta learning
- vc dimension
- automatic model selection
- model selection criteria
- kernel learning
- statistical inference
- marginal likelihood
- generalization error
- kernel function
- error estimation
- selection criterion
- decision trees
- gaussian processes
- artificial neural networks
- parameter determination