LEADER 03592nam 22008291 450 001 9910823913803321 005 20211216210909.0 010 $a3-11-028649-1 024 7 $a10.1515/9783110286496 035 $a(CKB)2670000000432725 035 $a(EBL)1130324 035 $a(OCoLC)858762149 035 $a(SSID)ssj0001002364 035 $a(PQKBManifestationID)11592651 035 $a(PQKBTitleCode)TC0001002364 035 $a(PQKBWorkID)10997975 035 $a(PQKB)10286933 035 $a(MiAaPQ)EBC1130324 035 $a(DE-B1597)176522 035 $a(OCoLC)1002244134 035 $a(OCoLC)1004882893 035 $a(OCoLC)1011454550 035 $a(OCoLC)979955088 035 $a(OCoLC)987949528 035 $a(OCoLC)992507703 035 $a(OCoLC)999360248 035 $a(DE-B1597)9783110286496 035 $a(Au-PeEL)EBL1130324 035 $a(CaPaEBR)ebr10786156 035 $a(CaONFJC)MIL807843 035 $a(PPN)202078639 035 $a(EXLCZ)992670000000432725 100 $a20130531h20132013 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRegularization theory for ill-posed problems $eselected topics /$fby Shuai Lu, Sergei V. Pereverzev 210 1$aBerlin ;$aBoston :$cWalter de Gruyter,$d[2013] 210 4$dİ2013 215 $a1 online resource (304 p.) 225 1 $aInverse and ill-posed problems series ;$v58 300 $aDescription based upon print version of record. 311 0 $a3-11-028646-7 320 $aIncludes bibliographical references and index. 327 $tFront matter --$tPreface --$tContents --$tChapter 1. An introduction using classical examples --$tChapter 2. Basics of single parameter regularization schemes --$tChapter 3. Multiparameter regularization --$tChapter 4. Regularization algorithms in learning theory --$tChapter 5. Meta-learning approach to regularization - case study: blood glucose prediction --$tBibliography --$tIndex 330 $aThis monograph is a valuable contribution to the highly topical and extremely productive field of regularization methods for inverse and ill-posed problems. The author is an internationally outstanding and accepted mathematician in this field. In his book he offers a well-balanced mixture of basic and innovative aspects. He demonstrates new, differentiated viewpoints, and important examples for applications. The book demonstrates the current developments in the field of regularization theory, such as multi parameter regularization and regularization in learning theory. The book is written for graduate and PhDs 410 0$aInverse and ill-posed problems series ;$vv. 58. 606 $aNumerical analysis$xImproperly posed problems 606 $aNumerical differentiation 610 $aBalancing Principle. 610 $aBlood Glucose Prediction. 610 $aConvergence Rate. 610 $aDiscrepancy Principle. 610 $aError Bound Estimation. 610 $aIll-posed Problem. 610 $aLearning Theory, Meta-learning. 610 $aMulti-parameter Regularization. 610 $aRegularization Method. 615 0$aNumerical analysis$xImproperly posed problems. 615 0$aNumerical differentiation. 676 $a518/.53 700 $aLu$b Shuai$f1976-$01648931 701 $aPereverzev$b Sergei V$01180221 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910823913803321 996 $aRegularization theory for ill-posed problems$93997386 997 $aUNINA