티호노프 정칙화

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  1. Typically for ridge regression, two departures from Tikhonov regularization are described.[1]
  2. In this work, we will use the Tikhonov regularization method to identify the space-dependent source for the time-fractional diffusion equation on a columnar symmetric domain.[2]
  3. This study compares traditional Tikhonov regularization with an extension of Tikhonov regularization which updates the solution found by the usual method.[3]
  4. The working methodology consists of adapting the Tikhonov regularization method for the paradigm of tuning the parameters of a conic given a set of coordinates in the two-dimensional plane.[4]
  5. Relationship between Tikhonov regularization and the compressive sensing is established.[5]
  6. The Tikhonov regularization method as implemented in PEST automatically generates a number of “information" equations, which defines the initial value of each parameter as the preferred value.[6]
  7. The generalized Tikhonov regularization method is proposed to solve this problem.[7]
  8. When the regularization matrix is a scalar multiple of the identity matrix, this is known as Ridge Regression.[8]
  9. The general case, with an arbitrary regularization matrix (of full rank) is known as Tikhonov regularization.[8]
  10. Tikhonov regularization in standardized and general form for multivariate calibration with application towards removing unwanted spectral artifacts.[8]
  11. Our approach is described within the Tikhonov regularization (TR) framework for linear regression model building, and our focus is on ignoring the influence of noninformative polynomial trends.[9]
  12. It is remarkable that these limits can also been obtained by expressing as a linear combination of sinc functions and then applying the Tikhonov regularization method in the frequency space.[10]
  13. Tikhonov regularization and truncated singular value decomposition (TSVD) are two elementary techniques for solving a least squares problem from a linear discrete ill-posed problem.[11]
  14. Tikhonov Regularization (sometimes called Tikhonov-Phillips regularization) is a popular way to deal with linear discrete ill-posed problems.[12]
  15. Tikhonov regularization, named for Andrey Tikhonov, is a method of regularization of ill-posed problems.[13]
  16. Tikhonov regularization has been invented independently in many different contexts.[13]
  17. Typically discrete linear ill-conditioned problems result from discretization of integral equations, and one can formulate a Tikhonov regularization in the original infinite-dimensional context.[13]
  18. A two-stage multi-damage detection approach for composite structures using MKECR-Tikhonov regularization iterative method and model updating procedure.[14]
  19. –Marquardt algorithm with improved Tikhonov regularization.[14]
  20. A novel Tikhonov regularization-based iterative method for structural damage identification of offshore platforms.[14]
  21. Simple and Efficient Determination of the Tikhonov Regularization Parameter Chosen by the Generalized Discrepancy Principle for Discrete Ill-Posed Problems.[14]
  22. When there are no prior information provided about the unknown epicardial potentials, the Tikhonov regularization method seems to be the most commonly used technique.[15]
  23. The two-norm Tikhonov regularization method (from now on referred to as Tikhonov) constrains the solution to be smooth or to have a small signal energy resolution.[15]
  24. The Tikhonov regularization parameter weights the residual norm against the solution norm.[15]
  25. However, it becomes challenging to find an automatic regularization parameter-choice method for Tikhonov regularization that is suitable for all ill-posed inverse problems (Hansen, 2010).[15]
  26. We will propose that finite element method complemented with Tikhonov regularization—a basic tool for inverse problems—is a powerful combination for further accuracy improvements.[16]
  27. In Section 2, Tikhonov regularization is presented.[16]
  28. Let us point out that in the literature, Tikhonov regularization is seldom presented in the form (5).[16]
  29. In this section, we will discuss some practical aspects of Tikhonov regularization with L-curve criterion, when it is applied to inertial navigation.[16]
  30. We adopt the Tikhonov regularization method by a reproducing kernel Hilbert space into the backward problem (1).[17]
  31. The Tikhonov regularization replaces the minimization problem (25) by the solution of a penalized least-squares problem with regularization parameter .[17]

소스

  1. ↑ Is Tikhonov regularization the same as Ridge Regression?
  2. ↑ Tikhonov regularization method for identifying the space-dependent source for time-fractional diffusion equation on a columnar symmetric domain
  3. ↑ Extension of Tikhonov regularization based on varying the singular values of the regularization operator
  4. ↑ Tuning of conic parameters using Tikhonov regularization and L-Curve simulation
  5. ↑ ON TIKHONOV REGULARIZATION AND COMPRESSIVE SENSING FOR SEISMIC SIGNAL PROCESSING
  6. ↑ FePEST 7.1 Documentation
  7. ↑ The Generalized Tikhonov Regularization Method for High Order Numerical Derivatives
  8. ↑ 8.0 8.1 8.2 njchiang/tikhonov: code for L2 regularization of arbitrary Tikhonov matrices
  9. ↑ Baseline and interferent correction by the Tikhonov regularization framework for linear least squares modeling
  10. ↑ Finite Dimensional Approximation and Tikhonov Regularization Method
  11. ↑ A modified Tikhonov regularization method ☆
  12. ↑ Tikhonov Regularization: Simple Definition
  13. ↑ 13.0 13.1 13.2 Tikhonov regularization
  14. ↑ 14.0 14.1 14.2 14.3 SIAM Journal on Matrix Analysis and Applications
  15. ↑ 15.0 15.1 15.2 15.3 Considering New Regularization Parameter-Choice Techniques for the Tikhonov Method to Improve the Accuracy of Electrocardiographic Imaging
  16. ↑ 16.0 16.1 16.2 16.3 Use of Tikhonov Regularization to Improve the Accuracy of Position Estimates in Inertial Navigation
  17. ↑ 17.0 17.1 A Discretized Tikhonov Regularization Method for a Fractional Backward Heat Conduction Problem

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Spacy 패턴 목록

  • [{'LOWER': 'tikhonov'}, {'LEMMA': 'regularization'}]
  • [{'LOWER': 'ridge'}, {'LEMMA': 'regression'}]
  • [{'LOWER': 'weight'}, {'LEMMA': 'decay'}]
  • [{'LOWER': 'tikhonov'}, {'OP': '*'}, {'LOWER': 'miller'}, {'LEMMA': 'method'}]
  • [{'LOWER': 'phillips'}, {'OP': '*'}, {'LOWER': 'twomey'}, {'LEMMA': 'method'}]
  • [{'LOWER': 'constrained'}, {'LOWER': 'linear'}, {'LEMMA': 'inversion'}]
  • [{'LOWER': 'constrained'}, {'LOWER': 'linear'}, {'LOWER': 'inversion'}, {'LEMMA': 'method'}]
  • [{'LOWER': 'linear'}, {'LEMMA': 'regularization'}]
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