Assigning a class to a measurement, or equivalently, identifying the probabilistic source of a measurement. The only statistical model that is needed is the conditional model of the class variable given the measurement. This conditional model can be obtained from a joint model or it can be learned directly. The former approach is generative since it models the measurements in each class. It is more work, but it can exploit more prior knowledge, needs less data, is more modular, and can handle missing or corrupted data. Methods include mixture models and Hidden Markov Models. The latter approach is discriminative since it focuses only on discriminating one class from another. It can be more efficient once trained and requires fewer modeling assumptions. Methods include logistic regression, generalized linear classifiers, and nearest-neighbor. See "Discriminative and Learning".
