Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Machine Learning
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A hierarchical method for multi-class support vector machines
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Neural Networks - 2005 Special issue: IJCNN 2005
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
A support vector hierarchical method for multi-class classification and rejection
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Expert Systems with Applications: An International Journal
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We propose a new hierarchical design method, weighted support vector (WSV) k-means clustering, to design a binary hierarchical classification structure. This method automatically selects the classes to be separated at each node in the hierarchy, and allows visualization of clusters of high-dimensional support vector data; no prior hierarchical designs address this. At each node in the hierarchy, we use 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 SVMs). We give the basis and new insight into why a Gaussian kernel provides good rejection. Recognition and rejection test results on a real IR (infrared) database show that our proposed method outperforms the standard one-vs-rest methods and the use of standard SVM classifiers.