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Gradient Optimization for multiple kernel's parameters in support vector machines classification

The subject of this work is the model selection of kernels with multiple parameters for support vector machines (SVM), with the purpose of classifying hyperspectral remote sensing data. During the training process, the kernel parameters need to be tuned properly. In this work a gradient descent based algorithm is used to estimate the parameters. The selection of multiple parameters is addressed, and an approach based on the analysis of the variance values of individual bands was proposed. Several state of the art kernels were tested. Experiments were conducted on real hyperspectral data. Results obtained with the different approaches/kernels were compared statistically, and showed good results in terms classification accuracies and processing time.