Online Learning with (Multiple) Kernels: A Review

@article{Diethe2013OnlineLW,
  title={Online Learning with (Multiple) Kernels: A Review},
  author={Tom Diethe and Mark A. Girolami},
  journal={Neural Computation},
  year={2013},
  volume={25},
  pages={567-625}
}
This review examines kernel methods for online learning, in particular, multiclass classification. We examine margin-based approaches, stemming from Rosenblatt's original perceptron algorithm, as well as nonparametric probabilistic approaches that are based on the popular gaussian process framework. We also examine approaches to online learning that use combinations of kernels—online multiple kernel learning. We present empirical validation of a wide range of methods on a protein fold… CONTINUE READING
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Consistency of the group lasso and multiple kernel learning

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