03592nam 22008291 450 991082391380332120211216210909.03-11-028649-110.1515/9783110286496(CKB)2670000000432725(EBL)1130324(OCoLC)858762149(SSID)ssj0001002364(PQKBManifestationID)11592651(PQKBTitleCode)TC0001002364(PQKBWorkID)10997975(PQKB)10286933(MiAaPQ)EBC1130324(DE-B1597)176522(OCoLC)1002244134(OCoLC)1004882893(OCoLC)1011454550(OCoLC)979955088(OCoLC)987949528(OCoLC)992507703(OCoLC)999360248(DE-B1597)9783110286496(Au-PeEL)EBL1130324(CaPaEBR)ebr10786156(CaONFJC)MIL807843(PPN)202078639(EXLCZ)99267000000043272520130531h20132013 uy 0engurcnu||||||||txtccrRegularization theory for ill-posed problems selected topics /by Shuai Lu, Sergei V. PereverzevBerlin ;Boston :Walter de Gruyter,[2013]©20131 online resource (304 p.)Inverse and ill-posed problems series ;58Description based upon print version of record.3-11-028646-7 Includes bibliographical references and index.Front matter --Preface --Contents --Chapter 1. An introduction using classical examples --Chapter 2. Basics of single parameter regularization schemes --Chapter 3. Multiparameter regularization --Chapter 4. Regularization algorithms in learning theory --Chapter 5. Meta-learning approach to regularization - case study: blood glucose prediction --Bibliography --IndexThis monograph is a valuable contribution to the highly topical and extremely productive field of regularization 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 demonstrates the current developments in the field of regularization theory, such as multi parameter regularization and regularization in learning theory. The book is written for graduate and PhDsInverse and ill-posed problems series ;v. 58.Numerical analysisImproperly posed problemsNumerical differentiationBalancing Principle.Blood Glucose Prediction.Convergence Rate.Discrepancy Principle.Error Bound Estimation.Ill-posed Problem.Learning Theory, Meta-learning.Multi-parameter Regularization.Regularization Method.Numerical analysisImproperly posed problems.Numerical differentiation.518/.53Lu Shuai1976-1648931Pereverzev Sergei V1180221MiAaPQMiAaPQMiAaPQBOOK9910823913803321Regularization theory for ill-posed problems3997386UNINA