LEADER 02488nam 2200709 a 450 001 9910778837803321 005 20230422041912.0 010 $a0-8262-6009-8 035 $a(CKB)111004368705058 035 $a(OCoLC)300788753 035 $a(CaPaEBR)ebrary10001741 035 $a(SSID)ssj0000132950 035 $a(PQKBManifestationID)11129748 035 $a(PQKBTitleCode)TC0000132950 035 $a(PQKBWorkID)10041284 035 $a(PQKB)11574064 035 $a(MiAaPQ)EBC3570679 035 $a(Au-PeEL)EBL3570679 035 $a(CaPaEBR)ebr10001741 035 $a(OCoLC)932325616 035 $a(EXLCZ)99111004368705058 100 $a19980903d1999 ub 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aCrossing borders through folklore$b[electronic resource] $eAfrican American women's fiction and art /$fAlma Jean Billingslea-Brown 210 $aColumbia $cUniversity of Missouri Press$dc1999 215 $a1 online resource (160 p.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-8262-1199-2 320 $aIncludes bibliographical references (p. 125-142) and index. 606 $aAmerican fiction$xAfrican American authors$xHistory and criticism 606 $aAmerican fiction$xWomen authors$xHistory and criticism 606 $aLiterature and folklore$zUnited States 606 $aWomen and literature$zUnited States 606 $aAfrican American women in literature 606 $aAfrican Americans in literature 606 $aAfrican American women artists 606 $aAfrican Americans$vFolklore 606 $aAfrican American art 606 $aFolklore in art 615 0$aAmerican fiction$xAfrican American authors$xHistory and criticism. 615 0$aAmerican fiction$xWomen authors$xHistory and criticism. 615 0$aLiterature and folklore 615 0$aWomen and literature 615 0$aAfrican American women in literature. 615 0$aAfrican Americans in literature. 615 0$aAfrican American women artists. 615 0$aAfrican Americans 615 0$aAfrican American art. 615 0$aFolklore in art. 676 $a813.009/9287 700 $aBillingslea-Brown$b Alma Jean$f1946-$01573458 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910778837803321 996 $aCrossing borders through folklore$93849212 997 $aUNINA LEADER 04964nam 2200937z- 450 001 9910557359003321 005 20220111 035 $a(CKB)5400000000042302 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/77114 035 $a(oapen)doab77114 035 $a(EXLCZ)995400000000042302 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aStatistical Data Modeling and Machine Learning with Applications 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (184 p.) 311 08$a3-0365-2692-7 311 08$a3-0365-2693-5 330 $aThe modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section "Mathematics and Computer Science". Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties. 606 $aInformation technology industries$2bicssc 610 $aartificial neural networks 610 $aassessment 610 $abanking 610 $abrain-computer interface 610 $abreast cancer subtyping 610 $aCART ensembles and bagging 610 $acategorical data 610 $acitizen science 610 $aclassification 610 $aclassification and regression tree 610 $aclustering 610 $aCNN-LSTM architectures 610 $aconsensus models 610 $aconvexity 610 $across-validation 610 $adam inflow prediction 610 $adamped Newton 610 $adata quality 610 $adata-adaptive kernel functions 610 $adeep forest 610 $aEEG motor imagery 610 $aensemble model 610 $afeature selection 610 $aGower's interpolation formula 610 $aGower's metric 610 $ahedonic prices 610 $ahousing 610 $ahyper-parameter optimization 610 $aimage data 610 $ainput predictor selection 610 $akernel clustering 610 $akernel density estimation 610 $along short-term memory 610 $amachine learning 610 $amathematical competency 610 $aMETABRIC dataset 610 $amixed data 610 $amulti-category classifier 610 $amulti-omics data 610 $amultidimensional scaling 610 $amultivariate adaptive regression splines 610 $an/a 610 $anon-linear optimization 610 $apredictive models 610 $aquantile regression 610 $areal-time motion imagery recognition 610 $asimilarity 610 $astochastic gradient descent 610 $asupport vector machine 610 $awavelet transform 615 7$aInformation technology industries 700 $aGocheva-Ilieva$b Snezhana$4edt$01303375 702 $aGocheva-Ilieva$b Snezhana$4oth 906 $aBOOK 912 $a9910557359003321 996 $aStatistical Data Modeling and Machine Learning with Applications$93026963 997 $aUNINA