Abstract
When there are several sources of information available in pattern recognition problems, the task of combining them is most interesting. In the context of kernel methods it means to design a single kernel function that collects all the relevant information of each kernel for the classification task at hand. The problem is then solved by training a Support Vector Machine (SVM) on the resulting kernel. Here we propose a consistent method to produce kernel functions from kernel matrices created by any given kernel combination technique. Once this fusion kernel function is available, it will be possible to evaluate the kernel at any data point. The performance of the proposed fusion Kernel is illustrated on several classification and visualization tasks.
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Muñoz, A., González, J. (2008). Functional Learning of Kernels for Information Fusion Purposes. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2008. Lecture Notes in Computer Science, vol 5197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85920-8_34
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DOI: https://doi.org/10.1007/978-3-540-85920-8_34
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