Online Prediction of Time Series Data With Kernels

  title={Online Prediction of Time Series Data With Kernels},
  author={Chafic Saide and R{\'e}gis Lengell{\'e} and Paul Honeine and C{\'e}dric Richard and Roger Achkar},
  journal={IEEE Transactions on Signal Processing},
Kernel-based algorithms have been a topic of considerable interest in the machine learning community over the last ten years. Their attractiveness resides in their elegant treatment of nonlinear problems. They have been successfully applied to pattern recognition, regression and density estimation. A common characteristic of kernel-based methods is that they deal with kernel expansions whose number of terms equals the number of input data, making them unsuitable for online applications… CONTINUE READING
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