A sequential distribution free pattern classification procedure

A sequential distribution free pattern classification procedure is presented to classify unknown samples into one of two inseparable classes for the case where the underlying probability density functions of the classes are unknown. The algorithm is formed from training sets of known classification. An estimate (on an expected value basis) is given of the probability of making an error in classification by using order statistics.