The learning behavior of single neuron classifiers on linearly separable or nonseparable input

Determining linear separability is an important way of understanding structures present in data. We explore 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 implementation problems which make them less preferable than linear programming