Given the "normal" training examples, we next train and tune the hyperparameters for an SVDD anomaly detector. See Box 3 for mathematical details. The decision boundary of the trained and hyperparameter-tuned SVDD in sensor array response space is shown as the closed, black curve in Fig. . The response vectors falling inside the boundary are classified by the SVDD as normal; those falling outside are classified as anomalous. Of the 100 normal data used to train the SVDD, four are located outside the boundary and were misclassified as anomalies.