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Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Neural Networks and Fuzzy Systems: Theory and Applications
Neural Networks and Fuzzy Systems: Theory and Applications
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Training of support vector machines with Mahalanobis kernels
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Parsimonious Mahalanobis kernel for the classification of high dimensional data
Pattern Recognition
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In our previous work we have shown that Mahalanobis kernels are useful for support vector classifiers both from generalization ability and model selection speed. In this paper we propose using Mahalanobis kernels for function approximation. We determine the covariance matrix for the Mahalanobis kernel using all the training data. Model selection is done by line search. Namely, first the margin parameter and the error threshold are optimized and then the kernel parameter is optimized. According to the computer experiments for four benchmark problems, estimation performance of a Mahalanobis kernel with a diagonal covariance matrix optimized by line search is comparable to or better than that of an RBF kernel optimized by grid search.