@article {282,
	title = {Kernel Recursive Least-Squares Tracker for Time-Varying Regression},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	volume = {23},
	year = {2012},
	month = {August},
	pages = {1313--1326},
	abstract = {In this paper, we introduce a kernel 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 sensible 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 nonstationary scenarios. The resulting method is the first kernel adaptive filtering algorithm that includes a forgetting factor in a principled and numerically stable manner. In addition to its tracking ability, it has a number of appealing properties. It is online, requires a fixed amount of memory and computation per time step, incorporates regularization in a natural manner and provides confidence intervals along with each prediction. We include experimental results that support the theory as well as illustrate the efficiency of the proposed algorithm.},
	keywords = {kernel adaptive filtering, KRLS},
	issn = {2162-237X},
	doi = {10.1109/TNNLS.2012.2200500},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6227361},
	author = {Van Vaerenbergh, Steven and L{\'a}zaro-Gredilla, Miguel and Santamar{\'\i}a, Ignacio}
}
