LEADER 01664nam 2200529 a 450 001 9910458621503321 005 20200520144314.0 010 $a1-60780-460-3 035 $a(CKB)2560000000010316 035 $a(EBL)535633 035 $a(OCoLC)645093630 035 $a(SSID)ssj0000672290 035 $a(PQKBManifestationID)11367936 035 $a(PQKBTitleCode)TC0000672290 035 $a(PQKBWorkID)10635509 035 $a(PQKB)10446378 035 $a(OCoLC)748360698 035 $a(MiAaPQ)EBC535633 035 $a(Au-PeEL)EBL535633 035 $a(CaPaEBR)ebr10389163 035 $a(EXLCZ)992560000000010316 100 $a20150401h20101993 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBenin$b[electronic resource] $esociety & culture /$fWorld Trade Press 205 $a2nd ed. 210 $aPetaluma, Calif. $cWorld Trade Press$dc1993-2010 [2010] 215 $a1 online resource (24 p.) 300 $aCover title. 330 $aNeed to know it all? Our all-inclusive culture report for Benin will get up to speed on all aspects of culture in Benin, including lifecycle, religion, women, superstitions & folklore, sports, holidays & festivals, and etiquette. 606 $aWomen$zBenin 607 $aBenin$xSocial life and customs 607 $aBenin$xDescription and travel 607 $aBenin$xSocial conditions 608 $aElectronic books. 615 0$aWomen 676 $a966.83 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910458621503321 996 $aBenin$91999035 997 $aUNINA LEADER 04032nam 22006975 450 001 9910484138403321 005 20200701140551.0 010 $a3-030-13962-X 024 7 $a10.1007/978-3-030-13962-9 035 $a(CKB)4100000007810371 035 $a(MiAaPQ)EBC5733064 035 $a(DE-He213)978-3-030-13962-9 035 $a(PPN)243767560 035 $a(EXLCZ)994100000007810371 100 $a20190316d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStream Data Mining: Algorithms and Their Probabilistic Properties /$fby Leszek Rutkowski, Maciej Jaworski, Piotr Duda 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (331 pages) 225 1 $aStudies in Big Data,$x2197-6503 ;$v56 311 $a3-030-13961-1 327 $aIntroduction and Overview of the Main Results of the Book -- Basic concepts of data stream mining -- Decision Trees in Data Stream Mining -- Splitting Criteria based on the McDiarmid?s Theorem. 330 $aThis book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who deal with stream data, e.g. in telecommunication, banking, and sensor networks. 410 0$aStudies in Big Data,$x2197-6503 ;$v56 606 $aComputational intelligence 606 $aData mining 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aBig data 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aData mining. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aBig data. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aSignal, Image and Speech Processing. 615 24$aBig Data/Analytics. 615 24$aArtificial Intelligence. 676 $a006.312 700 $aRutkowski$b Leszek$4aut$4http://id.loc.gov/vocabulary/relators/aut$0477299 702 $aJaworski$b Maciej$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aDuda$b Piotr$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910484138403321 996 $aStream Data Mining: Algorithms and Their Probabilistic Properties$92855504 997 $aUNINA