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Proceedings Paper

New design method for a hierarchical SVM-based classifier
Author(s): Yu-Chiang Frank Wang; David Casasent
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Paper Abstract

We propose to use new SVM-type classifiers in a binary hierarchical tree classification structure to efficiently address the multi-class classification problem. A new hierarchical design method, WSV (weighted support vector) K-means Clustering, is presented; it automatically selects the classes to be separated at each node in the hierarchy. Our method is able to visualize and cluster high-dimensional support vector data; therefore, it improves upon prior hierarchical classifier designs. At each node in the hierarchy, we apply an SVRDM (support vector representation and discrimination machine) classifier, which offers generalization and good rejection of unseen false objects; rejection is not achieved with the standard SVM classifier. We provide the theoretical basis and insight into the choice of the Gaussian kernel to provide the SVRDM's rejection ability. New classification and rejection test results are presented on a real IR (infra-red) database.

Paper Details

Date Published: 10 September 2007
PDF: 13 pages
Proc. SPIE 6764, Intelligent Robots and Computer Vision XXV: Algorithms, Techniques, and Active Vision, 676402 (10 September 2007); doi: 10.1117/12.731424
Show Author Affiliations
Yu-Chiang Frank Wang, Carnegie Mellon Univ. (United States)
David Casasent, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 6764:
Intelligent Robots and Computer Vision XXV: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Röning, Editor(s)

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