Method for location of clusters of patterns to initialise a learning machine

Abstract

Pattern recognition has usually been regarded as a two-part process requiring both measurement and classification. It has also been accepted by many workers that the classifier can best be designed using an automatic learning process. However it is well known that many of the proposed learning schemes sometimes fail to realise the most efficient use of the available storage. This difficulty usually arises whenever the initial classifier was a poor approximation to the teacher. The design of the classifier using such learning techniques would clearly be more successful if a method could be found of providing a good starting state for the learning machine. The letter defines an algorithm and examines its performance in this task of initialising a learning machine.