Successive pattern classification based on test feature classifier and its application to defect image classification
Pages 1847 - 1856
Abstract
A novel successive learning algorithm based on a Test Feature Classifier is proposed for efficient handling of sequentially provided training data. The fundamental characteristics of the successive learning are considered. In the learning, after recognition of a set of unknown data by a classifier, they are fed into the classifier in order to obtain a modified performance. An efficient algorithm is proposed for the incremental definition of prime tests which are irreducible combinations of features and capable of classifying training patterns into correct classes. Four strategies for addition of training patterns are investigated with respect to their precision and performance using real pattern data. A real-world problem of classification of defects on wafer images has been dealt with by the proposed classifier, obtaining excellent performance even through efficient addition strategies.
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- Successive pattern classification based on test feature classifier and its application to defect image classification
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Copyright © Pattern Recognition Society © 2005.
Publisher
Elsevier Science Inc.
United States
Publication History
Published: 01 November 2005
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