Typically, the learning curve (performance of an anomaly detector as a function of the size of the data set used for training it) increases rapidly at small data sizes, then reaches diminishing returns and saturates as more data is used for training. The amount of training data needed to reach diminishing returns for an anomaly detector for gas sensor arrays, likely, depends on the sensor array and task.