  The generalization performance of kernel methods is largely determined by the
kernel, but common kernels are stationary thus input-independent and
output-independent, that limits their applications on complicated tasks. In
this paper, we propose a powerful and efficient spectral kernel learning
framework and learned kernels are dependent on both inputs and outputs, by
using non-stationary spectral kernels and flexibly learning the spectral
measure from the data. Further, we derive a data-dependent generalization error
bound based on Rademacher complexity, which estimates the generalization
ability of the learning framework and suggests two regularization terms to
improve performance. Extensive experimental results validate the effectiveness
of the proposed algorithm and confirm our theoretical results.
