Add to ORKGThismonograph is a valuable contribution to thehighly topical and extremly productive field ofregularisationmethods for inverse and ill-posed problems. The author is an internationally outstanding and acceptedmathematicianin this field. In his book he offers a well-balanced mixtureof basic and innovative aspects.He demonstrates new,differentiatedviewpoints, and important examples for applications. The bookdemontrates thecurrent developments inthe field of regularization theory,such as multiparameter regularization and regularization in learning theory. The book is written for graduate and PhD
Ill-posed inverse problem solving using two methods of choosing of regularization paramete
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
We present a strategy for choosing the regularization parameter (Lepskij-type balancing principle) fo...
Many works have shown that strong connections relate learning from ex- amples to regularization tech...
Many works have shown that strong connections relate learning from examples to regularization techni...
Many works have shown that strong connections relate learning from examples to regularization techni...
The regularization of ill-posed systems of equations is carried out by corrections of the data or th...
In this paper we discuss a relation between Learning Theory and Regularization of linear ill-posed i...
AbstractIn this paper we discuss a relation between Learning Theory and Regularization of linear ill...
The focus of this book is on "ill-posed inverse problems". These problems cannot be solved only on t...
The advent of the computer had forced the application of mathematics to all branches of human endeav...
Regularization methods are a key tool in the solution of inverse problems. They are used to introduc...
Many works related learning from examples to regularization techniques for inverse problems, emphasi...
Many works related learning from examples to regularization techniques for inverse prob- lems, empha...
The solution of ill-posed problems is non-trivial in the sense that frequently applied methods like ...
Ill-posed inverse problem solving using two methods of choosing of regularization paramete
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
We present a strategy for choosing the regularization parameter (Lepskij-type balancing principle) fo...
Many works have shown that strong connections relate learning from ex- amples to regularization tech...
Many works have shown that strong connections relate learning from examples to regularization techni...
Many works have shown that strong connections relate learning from examples to regularization techni...
The regularization of ill-posed systems of equations is carried out by corrections of the data or th...
In this paper we discuss a relation between Learning Theory and Regularization of linear ill-posed i...
AbstractIn this paper we discuss a relation between Learning Theory and Regularization of linear ill...
The focus of this book is on "ill-posed inverse problems". These problems cannot be solved only on t...
The advent of the computer had forced the application of mathematics to all branches of human endeav...
Regularization methods are a key tool in the solution of inverse problems. They are used to introduc...
Many works related learning from examples to regularization techniques for inverse problems, emphasi...
Many works related learning from examples to regularization techniques for inverse prob- lems, empha...
The solution of ill-posed problems is non-trivial in the sense that frequently applied methods like ...
Ill-posed inverse problem solving using two methods of choosing of regularization paramete
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
We present a strategy for choosing the regularization parameter (Lepskij-type balancing principle) fo...