Regularization Theory for Ill-posed Problems : : Selected Topics /

Regularization Theory for Ill-posed Problems : : Selected Topics / / Sergei V. Pereverzev, Shuai Lu.

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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 an...

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Superior document:Title is part of eBook package: De Gruyter DGBA Backlist Complete English Language 2000-2014 PART1
VerfasserIn:
Lu, Shuai,
Pereverzev, Sergei V.,
Place / Publishing House:Berlin ;, Boston : : De Gruyter, , [2013]
©2013
Year of Publication:2013
Language:English
Series:Inverse and Ill-Posed Problems Series , 58
Online Access:
Physical Description:1 online resource (289 p.)
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245 1 0 |a Regularization Theory for Ill-posed Problems :  |b Selected Topics /  |c Sergei V. Pereverzev, Shuai Lu. 
264 1 |a Berlin ;  |a Boston :   |b De Gruyter,   |c [2013] 
264 4 |c ©2013 
300 |a 1 online resource (289 p.) 
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490 0 |a Inverse and Ill-Posed Problems Series ,  |x 1381-4524 ;  |v 58 
505 0 0 |t Frontmatter --   |t Preface --   |t Contents --   |t Chapter 1. An introduction using classical examples --   |t Chapter 2. Basics of single parameter regularization schemes --   |t Chapter 3. Multiparameter regularization --   |t Chapter 4. Regularization algorithms in learning theory --   |t Chapter 5. Meta-learning approach to regularization – case study: blood glucose prediction --   |t Bibliography --   |t Index 
506 0 |a restricted access  |u http://purl.org/coar/access_right/c_16ec  |f online access with authorization  |2 star 
520 |a 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. 
530 |a Issued also in print. 
538 |a Mode of access: Internet via World Wide Web. 
546 |a In English. 
588 0 |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 28. Feb 2023) 
650 0 |a Numerical analysis  |x Improperly posed problems. 
650 0 |a Numerical differentiation. 
650 7 |a MATHEMATICS / Applied.  |2 bisacsh 
653 |a Balancing Principle. 
653 |a Blood Glucose Prediction. 
653 |a Convergence Rate. 
653 |a Discrepancy Principle. 
653 |a Error Bound Estimation. 
653 |a Ill-posed Problem. 
653 |a Learning Theory, Meta-learning. 
653 |a Multi-parameter Regularization. 
653 |a Regularization Method. 
700 1 |a Pereverzev, Sergei V.,   |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
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773 0 8 |i Title is part of eBook package:  |d De Gruyter  |t E-BOOK PACKAGE MATHEMATICS, PHYSICS, ENGINEERING 2013  |z 9783110317282  |o ZDB-23-DMI 
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776 0 |c print  |z 9783110286465 
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