Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
RCE classifiers: theory and practice
Cybernetics and Systems
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rounding of polytopes in the real number model of computation
Mathematics of Operations Research
Primal-dual interior-point methods
Primal-dual interior-point methods
Learning Class Regions by the Union of Ellipsoids
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
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Prototype classifiers are one of the simplest and most intuitive approaches in pattern classification. However, they need careful positioning of prototypes to capture the distribution of each class region. Classical methods, such as learning vector quantization (LVQ), are sensitive to the initial choice of the number and the locations of the prototypes. To alleviate this problem, a new method is proposed that represents each class region by a set of compact hyperspheres. The number of hyperspheres and their locations are determined by setting up the problem as a set of quadratic optimization problems. Experimental results show that the proposed approach significantly beats LVQ and Restricted Coulomb Energy (RCE) in most performance aspects.