LEADER 03059nam 2200613Ia 450 001 9910783525303321 005 20230124181855.0 035 $a(CKB)1000000000243368 035 $a(OCoLC)137342122 035 $a(CaPaEBR)ebrary10112238 035 $a(SSID)ssj0000278244 035 $a(PQKBManifestationID)11219880 035 $a(PQKBTitleCode)TC0000278244 035 $a(PQKBWorkID)10242042 035 $a(PQKB)10759123 035 $a(Au-PeEL)EBL3306393 035 $a(CaPaEBR)ebr10112238 035 $a(CaSebORM)0738498653 035 $a(MiAaPQ)EBC3306393 035 $a(EXLCZ)991000000000243368 100 $a20040615d2003 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aConverting to DFSMSrmm from CA-1$b[electronic resource] /$f[Mary Lovelace, Norbert Schlumberger, Sue Hamner] 205 $a2nd ed. 210 $a[S.l.] $cIBM, International Technical Support Organization$dc2003 215 $a1 online resource (828 p.) 225 1 $aIBM redbooks 300 $a"December 2003." 311 $a0-7384-9865-3 320 $aIncludes bibliographical references and index. 330 $aDFSMSrmm is the IBM tape management system for OS/390 and z/OS platforms. As part of DFSMS, DFSMSrmm is completely integrated into the IBM storage management strategy. This allows an easier installation and maintenance, as well as standard interfaces with other systems components, such as DFSMSdfp and DFSMShsm. DFSMSrmm provides a simple and flexible tape management environment, with support for all tape technologies, including IBM automated tape libraries, manual tapes, and other tape libraries. This IBM Redbooks publication is written for people who are planning to convert from CA-1 to DFSMSrmm. We have designed this book to help you with all aspects of the conversion, from the early planning stage through implementation and customization of DFSMSrmm into your production system. We provide details on the differences between DFSMSrmm and CA-1 and compare the terminology, data, and functions. We explain how to use the IBM-supplied sample conversion programs, validate the converted data, and prepare it for use in a production environment. Working samples that are ready for use both during and after conversion are included. 410 0$aIBM redbooks. 606 $aMemory management (Computer science) 606 $aComputer storage devices 606 $aData tapes 615 0$aMemory management (Computer science) 615 0$aComputer storage devices. 615 0$aData tapes. 676 $a005.4/35 700 $aLovelace$b Mary$01469393 701 $aSchlumberger$b Norbert$01511139 701 $aHamner$b Sue$01511140 712 02$aInternational Business Machines Corporation.$bInternational Technical Support Organization. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910783525303321 996 $aConverting to DFSMSrmm from CA-1$93744204 997 $aUNINA LEADER 04052nam 22006015 450 001 9910254842703321 005 20200630154856.0 010 $a3-319-54765-8 024 7 $a10.1007/978-3-319-54765-7 035 $a(CKB)3850000000027371 035 $a(DE-He213)978-3-319-54765-7 035 $a(MiAaPQ)EBC6311638 035 $a(MiAaPQ)EBC5577452 035 $a(Au-PeEL)EBL5577452 035 $a(OCoLC)982655949 035 $a(PPN)200514784 035 $a(EXLCZ)993850000000027371 100 $a20170406d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOutlier Ensembles $eAn Introduction /$fby Charu C. Aggarwal, Saket Sathe 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XVI, 276 p. 55 illus., 9 illus. in color.) 311 $a3-319-54764-X 320 $aIncludes bibliographical references and index. 327 $aAn Introduction to Outlier Ensembles -- Theory of Outlier Ensembles -- Variance Reduction in Outlier Ensembles -- Bias Reduction in Outlier Ensembles: The Guessing Game -- Model Combination Methods for Outlier Ensembles -- Which Outlier Detection Algorithm Should I Use? 330 $aThis book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. In addition, it covers the techniques with which such methods can be made more effective. A formal classification of these methods is provided, and the circumstances in which they work well are examined. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification. The similarities and (subtle) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the design of ensemble algorithms for the latter problem. This book can be used for courses in data mining and related curricula. Many illustrative examples and exercises are provided in order to facilitate classroom teaching. A familiarity is assumed to the outlier detection problem and also to generic problem of ensemble analysis in classification. This is because many of the ensemble methods discussed in this book are adaptations from their counterparts in the classification domain. Some techniques explained in this book, such as wagging, randomized feature weighting, and geometric subsampling, provide new insights that are not available elsewhere. Also included is an analysis of the performance of various types of base detectors and their relative effectiveness. The book is valuable for researchers and practitioners for leveraging ensemble methods into optimal algorithmic design. 606 $aComputers 606 $aArtificial intelligence 606 $aStatistics 606 $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 615 0$aComputers. 615 0$aArtificial intelligence. 615 0$aStatistics. 615 14$aInformation Systems and Communication Service. 615 24$aArtificial Intelligence. 615 24$aStatistics and Computing/Statistics Programs. 676 $a005.1 700 $aAggarwal$b Charu C$4aut$4http://id.loc.gov/vocabulary/relators/aut$0518673 702 $aSathe$b Saket$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254842703321 996 $aOutlier Ensembles$92517720 997 $aUNINA