Kernel Recursive Least-Squares Tracker for Time-Varying Regression

  title={Kernel Recursive Least-Squares Tracker for Time-Varying Regression},
  author={Steven Van Vaerenbergh and Miguel L{\'a}zaro-Gredilla and Ignacio Santamar{\'i}a},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
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… CONTINUE READING
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Adaptive Filter Theory

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