Successive Pattern Learning based on Test Feature Classifier and its application to Defect Image Classification

Abstract

A novel sequential learning algorithm of Test Feature Classifier (TFC) which is non-parametric and effective even for small data is proposed for efficiently handling consecutively provided training data. Fundamental characteristics of the sequential learning are examined. In the learning, after recognition of a set of unknown objects, they are fed into the classifier in order to obtain a modified classifier. We propose an efficient algorithm for reconstruction of prime tests, which are irreducible combinations of features which are capable to discriminate training patterns into correct classes, is formalized in cases of addition and removal of training patterns. Some strategies for the modification of training patterns are investigated with respect to their precision and performance by use of real pattern data. A real world problem of classification of defects on wafer images has been tackled by the proposed classifier, obtaining excellent performance even through efficient modification strategies.