A representation theorem for linear pattern classifier training

Abstract

A new representation concept, named the teaching space approach, for the pattern classification training theory is proposed as an alternative to the feature space and the weight space approach used in the contemporary pattern classification theory. The concept is introduced formally by means of a representation theorem. A model of the training process is given by the theorem that makes transparent the essential factors of the pattern classification training. This result is significant in the development of a theory of teaching systems, which is relevant to areas such as pattern recognition, neural networks, associative memories, robot training, and human training.