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The kernel recursive least squares (KRLS), a nonlinear counterpart of the famed RLS algorithm, performs linear regression in a high-dimensional feature space induced by a Mercer kernel. Despite the growing interest in the KRLS for nonlinear signal processing, the presence of outliers in the estimation data causes the resulting predictor’s performance to deteriorate considerably. Bearing this in mind,...
Aiming at machine learning applications in which fast online learning is required, we develop a variant of the Least Squares SVR (LSSVR) model that can learn incrementally from data and eventually provide a sparse solution vector. This is possible by incorporating into the LSSVR model the sparsification mechanism used by the kernel RLS (KRLS) model introduced in Engel et al., 2004. The performance...
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