Nov 19, 2014 · We have presented a RKBR method that is insensitive to both Y-space and X-space outliers. The proposed method reduces the effect of X-space ...
The kernel-based regression (KBR) method is a useful tool for function estimation. One of the most popular KBR methods is the support vector machine for ...
KBR methods can be very sensitive to influential observations that in turn have a noticeable impact on the model coefficients. The robustness of KBR methods has ...
The kernel-based regression (KBR) method is a useful tool for function estimation. One of the most popular KBR methods is the support vector machine for ...
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In this section, we implement numerical experiments for our proposed regularized RDR scheme with some robust loss functions realized by appropriately chosen ...
Abstract. It is well-known that Kernel Based Regression (KBR) with a least squares loss has some undesirable properties from robustness point of view.
Robust kernel-based regression with bounded influence for outliers · Journal of the Operational Research Society 66(8): 1385-1398 · 2015 · Quick Search · Content.
Thus if the kernel is bounded and the first derivative of the loss function is bounded, then the influence function is bounded as well.
Abstract. It has been shown that Kernel Based Regression (KBR) with a least squares loss has some undesirable properties from robustness point of view.