NeurIPS Conference 2004 Conference Paper
Efficient Kernel Machines Using the Improved Fast Gauss Transform
- Changjiang Yang
- Ramani Duraiswami
- Larry 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.