### 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.

Original language | English (US) |
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Title of host publication | Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 |

Pages | 32-37 |

Number of pages | 6 |

DOIs | |

State | Published - Dec 1 2008 |

Event | 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico Duration: Oct 16 2008 → Oct 19 2008 |

### Publication series

Name | Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 |
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### Other

Other | 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 |
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Country | Mexico |

City | Cancun |

Period | 10/16/08 → 10/19/08 |

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## Cite this

*Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008*(pp. 32-37). [4685451] (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008). https://doi.org/10.1109/MLSP.2008.4685451