The SVDD may serve as a useful semi-supervised anomaly detector for gas sensor arrays. The training data for an SVDD is a collection of sensor array response vectors, all observed under normal conditions; response vectors to anomalous conditions are not needed. During training, the SVDD constructs the smallest possible hypersphere in response space that contains most of the normal response vectors. The idea is to circumscribe the support of the underlying distribution that generates the normal response vectors. During inference, a new response vector is classified as "normal" if it falls inside the hypersphere and "anomalous" if it falls outside of it. As the SVDD is a kernel method, we may leverage a menu of kernel functions to implicitly map our sensor response vectors into a higher-dimensional space, where the hypersphere is constructed. This gives access to more complex decision boundaries than a hypersphere in the original response space. Clustering algorithms could also identify anomalous gas compositions in an un-or partially-labeled data set of sensor array responses .