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- W2096838831 abstract "In this paper, we investigate the wellposedness of the kernel adaline. The kernel adaline finds the linear coefficients in a radial basis function network using deterministic gradient descent. We will show that the gradient descent provides an inherent regularization as long as the training is properly early-stopped. Along with other popular regularization techniques, this result is investigated in a unifying regularization-function concept. This understanding provides an alternative and possibly simpler way to obtain regularized solutions comparing with the cross-validation approach in regularization networks." @default.
- W2096838831 created "2016-06-24" @default.
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- W2096838831 date "2008-06-01" @default.
- W2096838831 modified "2023-09-25" @default.
- W2096838831 title "The wellposedness analysis of the kernel adaline" @default.
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- W2096838831 doi "https://doi.org/10.1109/ijcnn.2008.4633930" @default.
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