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**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*,
Ph.D. thesis, Computer Science Department, University of Tasmania,
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`

| `RegressionPartitionedSVM`

| `RegressionSVM`

| `fitrsvm`

| `kfoldLoss`

| `kfoldPredict`