Discard support vectors

`mdlOut = discardSupportVectors(mdl)`

returns
the trained, linear support vector machine (SVM) regression model `mdlOut`

= discardSupportVectors(`mdl`

)`mdlOut`

,
which is similar to the trained, linear SVM regression model `mdl`

,
except:

The

`Alpha`

and`SupportVectors`

properties are empty (`[]`

).If you display

`mdlOut`

, the software lists the`Beta`

property instead of the`Alpha`

property.

For a trained, linear SVM regression model, the `SupportVectors`

property
is an *n _{sv}*-by-

`Alpha`

property
is a vector with The `SupportVectors`

and `Alpha`

properties
can be large for complex data sets that contain many observations
or examples. However, the `Beta`

property is a vector
with *p* elements, which may be considerably smaller.
You can use a trained SVM regression model to predict response values
even if you discard the support vectors because the `predict`

and `resubPredict`

methods
use `Beta`

to compute the predicted responses.

If the trained, linear SVM regression model has many support
vectors, use `discardSupportVectors`

to reduce the
amount of disk space that the trained, linear SVM regression model
consumes. You can display the size of the support vector matrix by
entering `size(mdlIn.SupportVectors)`

.

The `predict`

and `resubPredict`

estimate
response values using the formula

$$f\left(x\right)=\left(\frac{X}{S}\right)\beta +{\beta}_{0}\text{\hspace{0.17em}},$$

where:

β is the Beta value, stored as

`mdl.Beta`

.β

_{0}is the bias value, stored as`mdl.Bias`

.`X`

is the training data.`S`

is the kernel scale value, stored as`mdl.KernelParameters.Scale`

.

In this way, the software can use the value of `mdl.Beta`

to
make predictions even after discarding the support vectors.

`CompactRegressionSVM`

| `RegressionSVM`

| `fitrsvm`

| `predict`

| `resubPredict`