Since classifcation is, at base, the task of recovering the model that generated the patterns, di?erent classi?cation techniques are useful depending on the type of candidate models themselves. In statistical pattern recognition we focus on the statistical properties of the patterns (generally expressed in probability densities), and this will command most of our attention in this book. Here the model for a pattern may be a single speci?c set of features, though the actual pattern sensed has been corrupted by some form of random noise. 