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Numerical Algorithms

, Volume 5, Issue 8, pp 375–390 | Cite as

Optimal estimation of linear operators from inaccurate data: A second look

  • Charles A. Micchelli
Smoothing and Regularization

Abstract

This paper intends to re-examine some results and proofs given in a previous publication on optimal estimation under uncertainty. In a rather general setting we showed that regularization of an element of a linear space relative to a quadratic criterion and inaccurate linear observations is an optimal method for recovering a linear operator of that element. For this to be the case, the regularization parameter must be chosen with care.

Keywords

General Setting Linear Operator Linear Space Regularization Parameter Optimal Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© J.C. Baltzer AG, Science Publishers 1993

Authors and Affiliations

  • Charles A. Micchelli
    • 1
  1. 1.IBM Research DivisionT.J. Watson Research CenterYorktown HeightsUSA

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