New design method for a hierarchical SVM-based classifier
Yu-Chiang Frank Wang, David P. Casasent · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
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.