Learning with Idealized Kernels

  title={Learning with Idealized Kernels},
  author={James T. Kwok and Ivor W. Tsang},
The kernel function plays a central role in kernel methods. Existing methods typically fix the functional form of the kernel in advance and then only adapt the associated kernel parameters based on empirical data. In this paper, we consider the problem of adapting the kernel so that it becomes more similar to the so-called ideal kernel. We formulate this as a distance metric learning problem that searches for a suitable linear transform (fcature weighting) in the kernel-induced feature space… CONTINUE READING
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