One Class SVM#
An unsupervised Support Vector Machine (SVM) used for anomaly detection. The One Class SVM aims to find a maximum margin between a set of data points and the origin, rather than between classes such as with SVC.
Note: This estimator requires the SVM extension which uses the libsvm engine under the hood.
Interfaces: Estimator, Learner
Data Type Compatibility: Continuous
Parameters#
| # | Param | Default | Type | Description |
|---|---|---|---|---|
| 1 | nu | 0.1 | float | An upper bound on the percentage of margin errors and a lower bound on the percentage of support vectors. |
| 2 | kernel | RBF | Kernel | The kernel function used to express non-linear data 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. |
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
Example#
use Rubix\ML\AnomalyDetectors\OneClassSVM;
use Rubix\ML\Kernels\SVM\Polynomial;
$estimator = new OneClassSVM(0.1, new Polynomial(4), true, 1e-3, 100.0);
References#
- C. Chang et al. (2011). LIBSVM: A library for support vector machines.