Support Vector Machine Pairwise Classifiers with Error Reduction for Image Classification Kingshy Goh, Edward Chang, and Kwang-Ting Cheng   View Paper (PDF)   Return to IMAGE RETRIEVAL I Abstract In this paper we study how Support Vector Machines (SVMs) can be applied to image classification. To enhance classification accuracy, we normalize SVM margins and apply variance reduction techniques to SVM pairwise classification results. From empirical study on a fifteen-category diversified image set, we show that combining pairwise SVMs and error reduction is an effective approach for image classification. This study is a critical step for our on-going effort on the development of a comprehensive approach, closely adapted to SVMs, to image classification. DiSC'02 © 2003 Association for Computing Machinery