@ARTICLE{Park2013g,
  author = {Park, Il Memming and Seth, Sohan and Van Vaerenbergh, Steven},
  title = {{Bayes}ian Extensions of Kernel Least Mean Squares},
  journal = {ArXiv e-prints},
  year = {2013},
  month = oct,
  abstract = {The kernel least mean squares ({KLMS}) algorithm is a computationally
	efficient nonlinear adaptive filtering method that "kernelizes" the
	celebrated (linear) least mean squares algorithm. We demonstrate
	that the least mean squares algorithm is closely related to the Kalman
	filtering, and thus, the {KLMS} can be interpreted as an approximate
	Bayesian filtering method. This allows us to systematically develop
	extensions of the {KLMS} by modifying the underlying state-space
	and observation models. The resulting extensions introduce many desirable
	properties such as "forgetting", and the ability to learn from discrete
	data, while retaining the computational simplicity and time complexity
	of the original algorithm.},
  archiveprefix = {arXiv},
  citeulike-article-id = {12732257},
  citeulike-linkout-0 = {http://arxiv.org/abs/1310.5347},
  citeulike-linkout-1 = {http://arxiv.org/pdf/1310.5347},
  day = {20},
  eprint = {1310.5347},
  keywords = {adaptive-filter, bayesian, kernel-method, klms, online-algorithm,
	poisson-observation},
  posted-at = {2013-10-23 12:18:03},
  primaryclass = {st.ML},
  priority = {0},
  url = {http://arxiv.org/abs/1310.5347}
}

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