A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
Probabilistic kernel least mean squares algorithms
2014
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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
doi:10.1109/icassp.2014.6855214
dblp:conf/icassp/ParkSV14
fatcat:rm6gllcivrfm5bhkht66xacnk4