@conference {Miguel_KRLS_MLSP2011,
	title = {A {Bayesian} Approach To Tracking With Kernel Recursive Least-Squares},
	booktitle = {IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2011)},
	year = {2011},
	month = {September},
	address = {Beijing, China},
	abstract = {In this paper we introduce a kernel-based recursive least-squares (KRLS) algorithm that is able to track nonlinear, time-varying relationships in data. To this purpose we first derive the standard KRLS equations from a Bayesian perspective (including a principled approach to pruning) and then take advantage of this framework to incorporate forgetting in a consistent way, thus enabling the algorithm to perform tracking in non-stationary scenarios. In addition to this tracking ability, the resulting algorithm has a number of appealing properties: It is online, requires a fixed amount of memory and computation per time step and incorporates regularization in a natural manner. We include experimental results that support the theory as well as illustrate the efficiency of the proposed algorithm.},
	keywords = {kernel adaptive filtering, KRLS},
	author = {L{\'a}zaro-Gredilla, Miguel and Van Vaerenbergh, Steven and Santamar{\'\i}a, Ignacio}
}
