Machine learning (ML) algorithms perform classification. Given a large set of sensor data, an ML algorithm determines a discriminant that can classify future sensor data into the correct classes. Most ML algorithms are statistical. A simple form of ML uses the means and variances of the data from two sensors to choose the sensor that produces the better discriminant. An optimal discriminant can be obtained by combining data from two sensors using linear discriminant analysis (LDA). LDA depends on statistical properties of the samples that do not always hold. When LDA is not appropriate, perceptrons, which are related to neural networks, can be used to perform classification.