|Download Help (Windows Only)|
Owning Palette: Anomaly Detection VIs
Requires: Analytics and Machine Learning Toolkit
Initializes the hyperparameters of the one-class support vector machine (SVM) algorithm. This VI uses the nu-SVM algorithm.
Use the one-class SVM model to estimate the boundary of a high-dimensional distribution. The one-class SVM algorithm trains models on data that has only one class.
|hyperparameters specifies the hyperparameters of the one-class SVM model. |
|error in describes error conditions that occur before this node runs. This input provides standard error in functionality.|
|untrained one-class SVM model returns the initialized one-class SVM model for training.|
|error out contains error information. This output provides standard error out functionality.|
Refer to the Anomaly Detection (Training) VI in the labview\examples\AML\Anomaly Detection directory for an example of using the Initialize Anomaly Detection Model (One-Class SVM) VI.