Localized algorithms for multiple kernel learning

  title={Localized algorithms for multiple kernel learning},
  author={Mehmet G{\"o}nen and Ethem Alpaydin},
  journal={Pattern Recognition},
Instead of selecting a single kernel, multiple kernel learning (MKL) uses a weighted sum of kernels where the weight of each kernel is optimized during training. Such methods assign the same weight to a kernel over the whole input space, and we discuss localized multiple kernel learning (LMKL) that is composed of a kernel-based learning algorithm and a parametric gating model to assign local weights to kernel functions. These two components are trained in a coupled manner using a two-step… CONTINUE READING
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