04964nam 2200937z- 450 991055735900332120220111(CKB)5400000000042302(oapen)https://directory.doabooks.org/handle/20.500.12854/77114(oapen)doab77114(EXLCZ)99540000000004230220202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierStatistical Data Modeling and Machine Learning with ApplicationsBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (184 p.)3-0365-2692-7 3-0365-2693-5 The 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.Information technology industriesbicsscartificial neural networksassessmentbankingbrain-computer interfacebreast cancer subtypingCART ensembles and baggingcategorical datacitizen scienceclassificationclassification and regression treeclusteringCNN-LSTM architecturesconsensus modelsconvexitycross-validationdam inflow predictiondamped Newtondata qualitydata-adaptive kernel functionsdeep forestEEG motor imageryensemble modelfeature selectionGower's interpolation formulaGower's metrichedonic priceshousinghyper-parameter optimizationimage datainput predictor selectionkernel clusteringkernel density estimationlong short-term memorymachine learningmathematical competencyMETABRIC datasetmixed datamulti-category classifiermulti-omics datamultidimensional scalingmultivariate adaptive regression splinesn/anon-linear optimizationpredictive modelsquantile regressionreal-time motion imagery recognitionsimilaritystochastic gradient descentsupport vector machinewavelet transformInformation technology industriesGocheva-Ilieva Snezhanaedt1303375Gocheva-Ilieva SnezhanaothBOOK9910557359003321Statistical Data Modeling and Machine Learning with Applications3026963UNINA