GPU acceleration for support vector machines

This paper presents a GPU-assisted version of the LIBSVM library for Support Vector Machines. SVMs are particularly popular for classification procedures among the research community, but for large training data the processing time becomes unrealistic. The modification that is proposed is porting the computation of the kernel matrix elements to the GPU, to significantly decrease the processing time for SVM training without altering the classification results compared to the original LIBSVM. The experimental evaluation of the proposed approach highlights how the GPU-accelerated version of LIBSVM enables the more efficient handling of large problems, such as large-scale concept detection in video.

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