SVC#
The multiclass Support Vector Machine (SVM) Classifier is a maximum margin classifier that can efficiently perform non-linear classification by implicitly mapping feature vectors into high-dimensional feature space using the kernel trick.
Note
This learner requires the SVM extension which uses the libsvm engine under the hood.
Interfaces: Estimator, Learner
Data Type Compatibility: Continuous
Parameters#
# | Name | Default | Type | Description |
---|---|---|---|---|
1 | c | 1.0 | float | The parameter that defines the width of the margin used to separate the classes. |
2 | kernel | RBF | Kernel | The kernel function used to operate in higher dimensions. |
3 | shrinking | true | bool | Should we use the shrinking heuristic? |
4 | tolerance | 1e-3 | float | The minimum change in the cost function necessary to continue training. |
5 | cache size | 100.0 | float | The size of the kernel cache in MB. |
Example#
use Rubix\ML\Classifiers\SVC;
use Rubix\ML\Kernels\SVM\Linear;
$estimator = new SVC(1.0, new Linear(), true, 1e-3, 100.0);
Additional Methods#
Save the model data to the filesystem:
public save(string $path) : void
Load the model data from the filesystem:
public load(string $path) : void
References#
-
C. Chang et al. (2011). LIBSVM: A library for support vector machines. ↩
Last update:
2021-03-03