A Generalized Kernel Approach to Structured Output Learning

@inproceedings{Kadri2013AGK,
  title={A Generalized Kernel Approach to Structured Output Learning},
  author={Hachem Kadri and Mohammad Ghavamzadeh and Philippe Preux},
  booktitle={ICML},
  year={2013}
}
We study the problem of structured output learning from a regression perspective. We first provide a general formulation of the kernel dependency estimation (KDE) approach to this problem using operator-valued kernels. Our formulation overcomes the two main limitations of the original KDE approach, namely the decoupling between outputs in the image space and the inability to use a joint feature space. We then propose a covariance-based operator-valued kernel that allows us to take into account… CONTINUE READING
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