LEADER 03916nam 22007575 450 001 996465521503316 005 20200705230719.0 010 $a3-319-49586-0 024 7 $a10.1007/978-3-319-49586-6 035 $a(CKB)3710000000981120 035 $a(DE-He213)978-3-319-49586-6 035 $a(MiAaPQ)EBC5577322 035 $a(PPN)197137288 035 $a(EXLCZ)993710000000981120 100 $a20161112d2016 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Data Mining and Applications$b[electronic resource] $e12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings /$fedited by Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XVI, 817 p. 280 illus.) 225 1 $aLecture Notes in Artificial Intelligence ;$v10086 300 $aIncludes index. 311 $a3-319-49585-2 330 $aThis book constitutes the proceedings of the 12th International Conference on Advanced Data Mining and Applications, ADMA 2016, held in Gold Coast, Australia, in December 2016. The 70 papers presented in this volume were carefully reviewed and selected from 105 submissions. The selected papers covered a wide variety of important topics in the area of data mining, including parallel and distributed data mining algorithms, mining on data streams, graph mining, spatial data mining, multimedia data mining, Web mining, the Internet of Things, health informatics, and biomedical data mining. 410 0$aLecture Notes in Artificial Intelligence ;$v10086 606 $aArtificial intelligence 606 $aData mining 606 $aInformation storage and retrieval 606 $aApplication software 606 $aDatabase management 606 $aComputer communication systems 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 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aInformation Systems Applications (incl. 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Internet). 615 24$aDatabase Management. 615 24$aComputer Communication Networks. 676 $a006.312 702 $aLi$b Jinyan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLi$b Xue$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWang$b Shuliang$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLi$b Jianxin$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSheng$b Quan Z$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465521503316 996 $aAdvanced Data Mining and Applications$9771989 997 $aUNISA LEADER 03515nam 22007215 450 001 9910299766803321 005 20251116135013.0 010 $a9783319189680 010 $a3319189689 024 7 $a10.1007/978-3-319-18968-0 035 $a(CKB)3710000000434251 035 $a(EBL)2096787 035 $a(SSID)ssj0001524808 035 $a(PQKBManifestationID)11869176 035 $a(PQKBTitleCode)TC0001524808 035 $a(PQKBWorkID)11484398 035 $a(PQKB)11344132 035 $a(DE-He213)978-3-319-18968-0 035 $a(MiAaPQ)EBC2096787 035 $a(PPN)186400268 035 $a(EXLCZ)993710000000434251 100 $a20150617d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian Nonparametric Data Analysis /$fby Peter Müller, Fernando Andres Quintana, Alejandro Jara, Tim Hanson 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (203 p.) 225 1 $aSpringer Series in Statistics,$x2197-568X 300 $aDescription based upon print version of record. 311 08$a9783319189673 311 08$a3319189670 320 $aIncludes bibliographical references and index at the end of each chapters. 327 $aPreface -- Acronyms -- 1.Introduction -- 2.Density Estimation - DP Models -- 3.Density Estimation - Models Beyond the DP -- 4.Regression -- 5.Categorical Data -- 6.Survival Analysis -- 7.Hierarchical Models -- 8.Clustering and Feature Allocation -- 9.Other Inference Problems and Conclusions -- Appendix: DP package. 330 $aThis book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book?s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages. 410 0$aSpringer Series in Statistics,$x2197-568X 606 $aStatistics 606 $aMathematical statistics$xData processing 606 $aBiometry 606 $aStatistical Theory and Methods 606 $aStatistics and Computing 606 $aBiostatistics 615 0$aStatistics. 615 0$aMathematical statistics$xData processing. 615 0$aBiometry. 615 14$aStatistical Theory and Methods. 615 24$aStatistics and Computing. 615 24$aBiostatistics. 676 $a519.542 700 $aMu?ller$b Peter$4aut$4http://id.loc.gov/vocabulary/relators/aut$0555240 702 $aQuintana$b Fernando Andre?s$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aJara$b Alejandro$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aHanson$b Tim$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299766803321 996 $aBayesian Nonparametric Data Analysis$92540385 997 $aUNINA