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Practical Tikhonov Regularized Estimators in Reproducing Kernel Hilbert Spaces for Statistical Inverse Problems
Tikhonov Regularized Estimators Reproducing Kernel Hilbert Spaces Statistical Inverse Problems
2013/6/13
Regularized kernel methods such as support vector machines (SVM) and support vector regression (SVR) constitute a broad and flexible class of methods which are theoretically well investigated and comm...
Efficient Estimators for Sequential and Resolution-Limited Inverse Problems
deconvolution ill-posed image processing sig-nal recovery
2012/9/18
A common problem in the sciences is that a signal of interest is observed only indirectly, through smooth functionals of the signal whose values are then obscured by noise. In suchinverse problems, th...
Needlet algorithms for estimation in inverse problems
statistical inverse problems minimax estimation second-generation wavelets
2009/9/16
We provide a new algorithm for the treatment of inverse problems which combines the traditional SVD inversion with an appropriate thresholding technique in a well chosen new basis. Our goal is to devi...
Adaptive complexity regularization for linear inverse problems
Inverse Problems Adaptive Estimation Regularization
2009/9/16
We tackle the problem of building adaptive estimation procedures for ill-posed inverse problems. For general regularization methods depending on tuning parameters, we construct a penalized method that...