LEADER 01518nam--2200469---450- 001 990002329740203316 005 20090826163205.0 035 $a000232974 035 $aUSA01000232974 035 $a(ALEPH)000232974USA01 035 $a000232974 100 $a20050104d1952----km-y0itay0103----ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aDiritto processuale civile$fEnrico Redenti 210 $aMilano$cGiuffrè$d1952- 215 $av.$d21 cm 327 $a : 1952. - 264 p. - : 1957. - 532 p. - : 1957. - 554 p. 410 0$12001 454 1$12001 461 1$1001-------$12001 700 1$aREDENTI,$bEnrico$068345 801 0$bsalbc$gISBD 912 $a990002329740203316 951 $aXXVII.1.B 53/1 (IG IX 130/I)$b40574 G.$cXXVII.1.B 53/1 (IG IX)$d00238438 951 $aXXVII.1.B 53/2 (IG IX 4/II)$b20846 G.$cXXVII.1.B 53/2 (IG IX)$d00238488 951 $aXXVII.1.B 53/3 (IG IX 4/III)$b20845 G.$cXXVII.1.B 53/3 (IG IX) 951 $aIV 39/1$b1410 DIRCE 951 $aIV 39/2$b1411 DIRCE 951 $aIV 39/3$b1412 DIRCE 959 $aBK 969 $aGIU 969 $aDIRCE 979 $aSIAV1$b10$c20050104$lUSA01$h1320 979 $aDIRCE$b90$c20070606$lUSA01$h1623 979 $aDIRCE$b90$c20070607$lUSA01$h0929 979 $aRSIAV2$b90$c20090825$lUSA01$h1108 979 $aRSIAV2$b90$c20090825$lUSA01$h1109 979 $aRSIAV5$b90$c20090826$lUSA01$h1632 996 $aDiritto processuale civile$961853 997 $aUNISA LEADER 04599nam 22007575 450 001 9910254212903321 005 20200705030139.0 010 $a9783319187815 010 $a3319187813 024 7 $a10.1007/978-3-319-18781-5 035 $a(CKB)3710000000454120 035 $a(SSID)ssj0001558406 035 $a(PQKBManifestationID)16183552 035 $a(PQKBTitleCode)TC0001558406 035 $a(PQKBWorkID)14818787 035 $a(PQKB)10600448 035 $a(DE-He213)978-3-319-18781-5 035 $a(MiAaPQ)EBC6296781 035 $a(MiAaPQ)EBC5587052 035 $a(Au-PeEL)EBL5587052 035 $a(OCoLC)913869402 035 $a(PPN)187689369 035 $a(EXLCZ)993710000000454120 100 $a20150707d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aChallenges in Computational Statistics and Data Mining /$fedited by Stan Matwin, Jan Mielniczuk 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (X, 399 p. 73 illus., 3 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v605 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a9783319187808 311 08$a3319187805 327 $aEvolutionary Computation for Real-world Problems -- Selection of Significant Features Using Monte Carlo Feature Selection -- ADX Algorithm for Supervised Classification -- Estimation of Entropy from Subword Complexity -- Exact Rates of Convergence of Kernel-based Classification Rule -- Compound Bipolar Queries: a Step Towards an Enhanced Human Consistency and Human Friendliness -- Process Inspection by Attributes Using Predicted Data -- Székely Regularization for Uplift Modeling -- Dominance-Based Rough Set Approach to Multiple Criterion Ranking with Sorting-specific Preference Information -- On things not Seen -- Network Capacity Bound for Personalized Bipartite Page Rank -- Dependence Factor as a Rule Evaluation Measure -- Recent Results on Quantlie Estimation Methods in Simulation Model -- Adaptive Monte Carlo Maximum Likelihood -- What Do we Choose when we Err? Model Selection and Testing for Misspecified Logistic Regression Revisited -- Semiparametric Inference Identification of Block-oriented Systems -- Dealing with Data Difficulty Factors While Learning from Imbalanced Data -- Privacy Protection in a Time of Big Data -- Data Based Modeling. 330 $aThis volume contains nineteen research papers belonging to the areas of computational statistics, data mining, and their applications. Those papers, all written specifically for this volume, are their authors? contributions to honour and celebrate Professor Jacek Koronacki on the occcasion of his 70th birthday. The book?s related and often interconnected topics, represent Jacek Koronacki?s research interests and their evolution. They also clearly indicate how close the areas of computational statistics and data mining are. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v605 606 $aComputational intelligence 606 $aData mining 606 $aStatistics 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 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aData mining. 615 0$aStatistics. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aArtificial Intelligence. 676 $a519.5 702 $aMatwin$b Stan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMielniczuk$b Jan$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254212903321 996 $aChallenges in Computational Statistics and Data Mining$91541113 997 $aUNINA