Efficient kernel machines using the improved fast Gauss transform

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.