Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The handbook of brain theory and neural networks
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Discriminative Dimensionality Reduction Based on Generalized LVQ
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
A Novel Kernel Prototype-Based Learning Algorithm
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
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Kernel Generalised Learning Vector Quantisation (KGLVQ) was proposed to extend Generalised Learning Vector Quantisation into the kernel feature space to deal with complex class boundaries and thus yield promising performance for complex classification tasks in pattern recognition. However KGLVQ does not follow the maximal margin principle which is crucial for kernel-based learning methods. In this paper we propose a maximal margin approach to Kernel Generalised Learning Vector Quantisation algorithm which inherits the merits of KGLVQ and follows the maximal margin principle to favour the generalisation capability. Experiments performed on the well-known data set III of BCI competition II show promising classification results for the proposed method.