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• W2078162554 abstract "This paper presents a novel framework for constrained adaptive learning in reproducing kernel Hilbert spaces (RKHS). A low complexity algorithmic solution is established. Constraints that encode a-priori information and several design specifications take the form of multiple intersecting closed convex sets. A cost function and the training data stream create a sequence of closed convex sets in the RKHS. The resulting recursive solution generates a sequence of estimates which converges to such an infinite intersection of closed convex sets. A time-adaptive beamforming task in an RKHS, rich in constraints, is also established. The numerical results show that the proposed method exhibits a significant improvement in resolution, when compared to the classical linear solution, and outperforms a recently unconstrained online kernel-based regression technique." @default.
• W2078162554 created "2016-06-24" @default.
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• W2078162554 date "2008-10-01" @default.
• W2078162554 modified "2023-09-28" @default.
• W2078162554 title "Constrained adaptive learning in Reproducing Kernel Hilbert Spaces: The beamforming paradigm" @default.
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• W2078162554 doi "https://doi.org/10.1109/mlsp.2008.4685451" @default.
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