LEADER 04931nam 2200913z- 450 001 9910557359003321 005 20231214133704.0 035 $a(CKB)5400000000042302 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/77114 035 $a(EXLCZ)995400000000042302 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Data Modeling and Machine Learning with Applications 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (184 p.) 311 $a3-0365-2692-7 311 $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 $amathematical competency 610 $aassessment 610 $amachine learning 610 $aclassification and regression tree 610 $aCART ensembles and bagging 610 $aensemble model 610 $amultivariate adaptive regression splines 610 $across-validation 610 $adam inflow prediction 610 $along short-term memory 610 $awavelet transform 610 $ainput predictor selection 610 $ahyper-parameter optimization 610 $abrain-computer interface 610 $aEEG motor imagery 610 $aCNN-LSTM architectures 610 $areal-time motion imagery recognition 610 $aartificial neural networks 610 $abanking 610 $ahedonic prices 610 $ahousing 610 $aquantile regression 610 $adata quality 610 $acitizen science 610 $aconsensus models 610 $aclustering 610 $aGower's interpolation formula 610 $aGower's metric 610 $amixed data 610 $amultidimensional scaling 610 $aclassification 610 $adata-adaptive kernel functions 610 $aimage data 610 $amulti-category classifier 610 $apredictive models 610 $asupport vector machine 610 $astochastic gradient descent 610 $adamped Newton 610 $aconvexity 610 $aMETABRIC dataset 610 $abreast cancer subtyping 610 $adeep forest 610 $amulti-omics data 610 $acategorical data 610 $asimilarity 610 $afeature selection 610 $akernel density estimation 610 $anon-linear optimization 610 $akernel clustering 615 7$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