Analysis of the distance between two classes for tuning SVM hyperparameters
IEEE Transactions on Neural Networks
Behavior-constrained support vector machines for fMRI data analysis
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Parsimonious Mahalanobis kernel for the classification of high dimensional data
Pattern Recognition
Self-advising support vector machine
Knowledge-Based Systems
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The performance of a support vector machine (SVM) largely depends on the kernel function used. This letter investigates a geometrical method to optimize the kernel function. The method is a modification of the one proposed by S. Amari and S. Wu. Its concern is the use of the prior knowledge obtained in a primary step training to conformally rescale the kernel function, so that the separation between the two classes of data is enlarged. The result is that the new algorithm works efficiently and overcomes the susceptibility of the original method