Efficient Kernel Machines Using the Improved Fast Gauss Transform
Changjiang Yang, Ramani Duraiswami, Larry Steven Davis · 2004
The computation required for kernel machines with N training samples is O(N ). Such computational 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.