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Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Robust fuzzy relational classifier incorporating the soft class labels
Pattern Recognition Letters
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
A simultaneous learning framework for clustering and classification
Pattern Recognition
A multiobjective simultaneous learning framework for clustering and classification
IEEE Transactions on Neural Networks
Support vector regression using mahalanobis kernels
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Parsimonious Mahalanobis kernel for the classification of high dimensional data
Pattern Recognition
Training mahalanobis kernels by linear programming
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
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Radial basis function (RBF) kernels are widely used for support vector machines. But for model selection, we need to optimize the kernel parameter and the margin parameter by time-consuming cross validation. To solve this problem, in this paper we propose using Mahalanobis kernels, which are generalized RBF kernels. We determine the covariance matrix for the Mahalanobis kernel using the training data corresponding to the associated classes. Model selection is done by line search. Namely, first the margin parameter is optimized and then the Mahalanobis kernel parameter is optimized. According to the computer experiments for two-class problems, a Mahalanobis kernel with a diagonal covariance matrix shows better generalization ability than a Mahalanobis kernel with a full covariance matrix, and a Mahalanobis kernel optimized by line search shows comparable performance with that with an RBF kernel optimized by grid search.