Class: RegressionSVM
Cross-validated support vector machine regression model
CVMdl = crossval(mdl)
CVMdl = crossval(mdl,Name,Value)
returns a cross-validated (partitioned) support vector machine regression model, CVMdl
= crossval(mdl
)CVMdl
, from a trained SVM regression model, mdl
.
returns a cross-validated model with additional options specified by one or more CVMdl
= crossval(mdl
,Name,Value
)Name,Value
pair arguments.
Instead of training an SVM regression model and then cross-validating it, you can create a cross-validated model directly by using fitrsvm
and specifying any of these name-value pair arguments: 'CrossVal'
, 'CVPartition'
, 'Holdout'
, 'Leaveout'
, or 'KFold'
.
[1] Nash, W.J., T. L. Sellers, S. R. Talbot, A. J. Cawthorn, and W. B. Ford. "The Population Biology of Abalone (Haliotis species) in Tasmania. I. Blacklip Abalone (H. rubra) from the North Coast and Islands of Bass Strait." Sea Fisheries Division, Technical Report No. 48, 1994.
[2] Waugh, S. "Extending and Benchmarking Cascade-Correlation: Extensions to the Cascade-Correlation Architecture and Benchmarking of Feed-forward Supervised Artificial Neural Networks." University of Tasmania Department of Computer Science thesis, 1995.
[3] Clark, D., Z. Schreter, A. Adams. "A Quantitative Comparison of Dystal and Backpropagation." submitted to the Australian Conference on Neural Networks, 1996.
[4] Lichman, M. UCI Machine Learning Repository, [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
CompactRegressionSVM
| fitrsvm
| kfoldLoss
| kfoldPredict
| RegressionPartitionedSVM
| RegressionSVM