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**Package: **classreg.learning.partition

**Superclasses: **`RegressionPartitionedModel`

Cross-validated support vector machine regression model

`RegressionPartitionedSVM`

is a set of support vector
machine (SVM) regression models trained on cross-validated folds.

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. `Name`

can also be a property name and `Value`

is the corresponding value.
`Name`

must appear inside single quotes (`''`

). You can
specify several name-value pair arguments in any order as
`Name1,Value1,...,NameN,ValueN`

.

kfoldLoss | Cross-validation loss of partitioned regression model |

kfoldPredict | Predict response for observations not used for training |

kfoldfun | Cross validate function |

You can create a `RegressionPartitionedSVM`

model using the following
techniques:

Use the training function

`fitrsvm`

and specify one of the`'CrossVal'`

,`'Holdout'`

,`'KFold'`

, or`'Leaveout'`

name-value pairs.Train a model using

`fitrsvm`

, then cross validate the model using the`crossval`

method.Create a cross validation partition using

`cvpartition`

, then pass the resulting partition object to`fitrsvm`

during training using the`'CVPartition'`

name-value pair.

[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.