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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

#	Name	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	cacheSize	100.0	float	The size of the kernel cache in MB.

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);

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

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1. C. Chang et al. (2011). LIBSVM: A library for support vector machines. ↩