A Unified View of Localized Kernel Learning

@inproceedings{Moeller2016AUV,
  title={A Unified View of Localized Kernel Learning},
  author={John Moeller and Sarathkrishna Swaminathan and Suresh Venkatasubramanian},
  booktitle={SDM},
  year={2016}
}
Multiple Kernel Learning, or MKL, extends (kernelized) SVM by attempting to learn not only a classifier/regressor but also the best kernel for the training task, usually from a combination of existing kernel functions. Most MKL methods seek the combined kernel that performs best over every training example, sacrificing performance in some areas to seek a global optimum. Localized kernel learning (LKL) overcomes this limitation by allowing the training algorithm to match a component kernel to… Expand
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