Linear Separability in Descent Procedures for Linear Classifiers

Determining linear separability is an important way to understand structures present in data. We review the behavior of several classical descent procedures for determining linear separability and training linear classifiers in the presence of linearly nonseparable input. We compare the adaptive procedures to linear programming methods using many pairwise discrimination problems from a public database. We found that the adaptive procedures have serious implementational problems which make them less preferable than linear programming.