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Training with Noise is Equivalent to Tikhonov Regularization
Neural Computation, 1995It is well known that the addition of noise to the input data of a neural network during training can, in some circumstances, lead to significant improvements in generalization performance. Previous work has shown that such training with noise is equivalent to a form of regularization in which an extra term is added to the error function. However, the
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On the Tikhonov regularization of affine pseudomonotone mappings
Optimization Letters, 2013The author gives some characterizations of the pseudomonotonicity in connection with the affine mappings on a nonempty closed convex subset \(K\subset \mathbb{R}^n\) and the non-negative orthant \(\mathbb{R}^{n}_{+}\), respectively. The author describes a class of affine pseudomonotone mappings whose regularized operators are not pseudomonotone.
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On Krylov projection methods and Tikhonov regularization [PDF]
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gazzola S, Novati P, Russo M.R.
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Recursive filtering 2D Tikhonov regularization
2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2022Hermes H. Ferreira, Eduardo S. L. Gastal
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Tikhonov Fixed—Point Regularization
2000The main purpose of this note is to propose viscosity approximation methods which amount to selecting a particular fixed-point of a given nonexpansive self mapping in a general Hilbert space. The connection with the selection principles of Attouch, in the context of convex minimization and monotone inclusion problems, is made and an application to a ...
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Iterated Tikhonov regularization with a general penalty term
Numerical Linear Algebra With Applications, 2017Marco Donatelli +2 more
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A new variant of L-curve for Tikhonov regularization
Journal of Computational and Applied Mathematics, 2009Mansoor Rezghi, S Mohammad Hosseini
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A Projection‐Based Approach to General‐Form Tikhonov Regularization
SIAM Journal of Scientific Computing, 2007Per Christian Hansen +2 more
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