Multiclass multiple kernel learning

@inproceedings{Zien2007MulticlassMK,
  title={Multiclass multiple kernel learning},
  author={Alexander Zien and Cheng Soon Ong},
  booktitle={ICML},
  year={2007}
}
In many applications it is desirable to learn from several kernels. "Multiple kernel learning" (MKL) allows the practitioner to optimize over linear combinations of kernels. By enforcing sparse coefficients, it also generalizes feature selection to kernel selection. We propose MKL for joint feature maps. This provides a convenient and principled way for MKL with multiclass problems. In addition, we can exploit the joint feature map to learn kernels on output spaces. We show the equivalence of… CONTINUE READING
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