Efficient Kernel Machines Using the Improved Fast Gauss Transform

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Changjiang Yang, Ramani Duraiswami, Larry S. Davis


The computation and memory required for kernel machines with N train- ing samples is at least O(N 2). Such a complexity is significant even for moderate size problems and is prohibitive for large datasets. We present an approximation technique based on the improved fast Gauss transform to reduce the computation to O(N ). We also give an error bound for the approximation, and provide experimental results on the UCI datasets.