@conference {vanvaerenbergh2006sliding,
	title = {A Sliding-Window Kernel {RLS} Algorithm and its Application to Nonlinear Channel Identification},
	booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)},
	year = {2006},
	month = {May},
	address = {Toulouse, France},
	abstract = {In this paper we propose a new kernel-based version of the recursive least-squares (RLS) algorithm for fast adaptive nonlinear filtering. Unlike other previous approaches, we combine a sliding-window approach (to fix the dimensions of the kernel matrix) with conventional -norm regularization (to improve generalization). The proposed kernel RLS algorithm is applied to a nonlinear channel identification problem (specifically, a linear filter followed by a memoryless nonlinearity), which typically appears in satellite communications or digital magnetic recording systems. We show that the proposed algorithm is able to operate in a time-varying environment and tracks abrupt changes in either the linear filter or the nonlinearity.},
	keywords = {kernel adaptive filtering, KRLS},
	author = {Van Vaerenbergh, Steven and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
