Separability is a Learner's Best Friend

Geometric separability is a generalisation of linear separability, familiar to many from Minsky and Papert’s analysis of the Perceptron learning method. The concept forms a novel dimension along which to conceptualise learning methods. The present paper shows how geometric separability can be defined and demonstrates that it accurately predicts the performance of a at least one empirical learning method.