LEADER 03496nam 22006855 450 001 9910741146803321 005 20230925103839.0 010 $a3-319-23696-2 024 7 $a10.1007/978-3-319-23696-4 035 $a(CKB)3710000000474273 035 $a(EBL)4178553 035 $a(SSID)ssj0001584752 035 $a(PQKBManifestationID)16265079 035 $a(PQKBTitleCode)TC0001584752 035 $a(PQKBWorkID)14865856 035 $a(PQKB)11100945 035 $a(DE-He213)978-3-319-23696-4 035 $a(MiAaPQ)EBC4178553 035 $a(PPN)190525983 035 $a(EXLCZ)993710000000474273 100 $a20150909d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRule Based Systems for Big Data $eA Machine Learning Approach /$fby Han Liu, Alexander Gegov, Mihaela Cocea 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (127 p.) 225 1 $aStudies in Big Data,$x2197-6503 ;$v13 300 $aDescription based upon print version of record. 311 $a3-319-23695-4 320 $aIncludes bibliographical references at the end of each chapters. 327 $aIntroduction -- Theoretical Preliminaries -- Generation of Classification Rules -- Simplification of Classification Rules -- Representation of Classification Rules -- Ensemble Learning Approaches -- Interpretability Analysis. 330 $aThe ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems. 410 0$aStudies in Big Data,$x2197-6503 ;$v13 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aData mining 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aData mining. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 676 $a004.21 700 $aLiu$b Han$4aut$4http://id.loc.gov/vocabulary/relators/aut$0665835 702 $aGegov$b Alexander$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aCocea$b Mihaela$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910741146803321 996 $aRule Based Systems for Big Data$93553655 997 $aUNINA LEADER 00993nam 22003733 450 001 9910915776303321 005 20230823000421.0 010 $a3-86980-546-3 035 $a(CKB)4100000011437955 035 $a(MiAaPQ)EBC6340447 035 $a(Au-PeEL)EBL6340447 035 $a(OCoLC)1195458402 035 $a(EXLCZ)994100000011437955 100 $a20210901d2020 uy 0 101 0 $ager 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDEUTUNGSHOHEIT $eDie Muster der Meinungsmacher 205 $a1st ed. 210 1$aGo?ttingen :$cBusinessVillage,$d2020. 210 4$dİ2020. 215 $a1 online resource (193 pages) 311 $a3-86980-545-5 517 $aDEUTUNGSHOHEIT 700 $aCallies$b Sebastian$01779402 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910915776303321 996 $aDEUTUNGSHOHEIT$94302748 997 $aUNINA