Regularization Theory for Ill-posed Problems
Selected Topics
- 303 pages
- English
- PDF
- Available on iOS & Android
About this book
This monograph is a valuable contribution to the highly topical and extremly productive field of regularisation methods for inverse and ill-posed problems. The author is an internationally outstanding and accepted mathematician in this field. In his book he offers a well-balanced mixture of basic and innovative aspects. He demonstrates new, differentiated viewpoints, and important examples for applications. The book demontrates the current developments in the field of regularization theory, such as multiparameter regularization and regularization in learning theory.
The book is written for graduate and PhD students and researchers in mathematics, natural sciences, engeneering, and medicine.
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Table of contents
- Preface
- 1 An introduction using classical examples
- 2 Basics of single parameter regularization schemes
- 3 Multiparameter regularization
- 4 Regularization algorithms in learning theory
- 5 Meta-learning approach to regularization – case study: blood glucose prediction
- Bibliography
- Index
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