LEADER 00891nam0-22003251i-450- 001 990002054260403321 005 20021010 035 $a000205426 035 $aFED01000205426 035 $a(Aleph)000205426FED01 035 $a000205426 100 $a20021010d--------km-y0itay50------ba 101 0 $aita 200 1 $aIntroduction to experimental ecology$fT. Lewis and L.R. Taylor 210 $aLondon$cAcademic Press$d1967 215 $a401 p.$d24 cm 610 0 $aEcologia 610 0 $aEcologia Sperimentale 610 0 $aEcologia Generale 676 $a574.5 700 1$aLewis,$bT.$064222 702 1$aTaylor,$bLionel Roy 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990002054260403321 952 $a61 VIII A.3/49$b2640 (27/6/67)$fDAGEN 959 $aDAGEN 996 $aIntroduction to Experimental Ecology$9379512 997 $aUNINA DB $aING01 LEADER 03264nam 22007331 450 001 9910462704103321 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(PPN)202078639 035 $a(Au-PeEL)EBL1130324 035 $a(CaPaEBR)ebr10786156 035 $a(CaONFJC)MIL807843 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 608 $aElectronic books. 615 0$aNumerical analysis$xImproperly posed problems. 615 0$aNumerical differentiation. 676 $a518/.53 700 $aLu$b Shuai$f1976-$01056775 701 $aPereverzev$b Sergei V$01056776 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910462704103321 996 $aRegularization theory for ill-posed problems$92491385 997 $aUNINA