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**Package: **classreg.learning.regr

Compact support vector machine regression model

`CompactRegressionSVM`

is a compact support vector machine (SVM)
regression model. It consumes less memory than a full, trained support vector machine model
(`RegressionSVM`

model) because it does not store the data used to train the
model.

Because the compact model does not store the training data, you cannot use it to perform certain tasks, such as cross validation. However, you can use a compact SVM regression model to predict responses using new input data.

returns a compact SVM regression model `compactMdl`

= compact(`mdl`

)`compactMdl`

from a full, trained
SVM regression model, `mdl`

. For more information, see `compact`

.

discardSupportVectors | Discard support vectors |

loss | Regression error for support vector machine regression model |

predict | Predict responses using support vector machine regression model |

Value. To learn how value classes affect copy operations, see Copying Objects (MATLAB).

[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.

`RegressionSVM`

| `compact`

| `fitrsvm`

| `plotPartialDependence`

| `update`