Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:496-503, 2009.
We propose hashing to facilitate efficient kernels. This generalizes previous work using sampling and we show a principled way to compute the kernel matrix for data streams and sparse feature spaces. Moreover, we give deviation bounds from the exact kernel matrix. This has applications to estimation on strings and graphs.