Practical Tikhonov Regularized Estimators in Reproducing Kernel Hilbert Spaces for Statistical Inverse Problems     Tikhonov Regularized Estimators  Reproducing Kernel Hilbert Spaces  Statistical Inverse Problems       font style='font-size:12px;'> 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...

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Efficient Estimators for Sequential and Resolution-Limited Inverse Problems     deconvolution  ill-posed  image processing  sig-nal recovery       font style='font-size:12px;'> 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...

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Needlet algorithms for estimation in inverse problems     statistical inverse problems  minimax estimation  second-generation wavelets       font style='font-size:12px;'> 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...

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Adaptive complexity regularization for linear inverse problems     Inverse Problems  Adaptive Estimation  Regularization       font style='font-size:12px;'> 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...

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