Explicit Approximations of the Gaussian Kernel
  We investigate training and using Gaussian kernel SVMs by approximating the
kernel with an explicit finite- dimensional polynomial feature representation
based on the Taylor expansion of the exponential. Although not as efficient as
the recently-proposed random Fourier features [Rahimi and Recht, 2007] in terms
of the number of features, we show how this polynomial representation can
provide a better approximation in terms of the computational cost involved.
This makes our "Taylor features" especially attractive for use on very large
data sets, in conjunction with online or stochastic training.
