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001 9910484138403321
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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