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024 7 _a10.1007/978-3-031-54468-2
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072 7 _aUN
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072 7 _aCOM021000
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245 1 0 _aRecent Trends and Future Challenges in Learning from Data
_h[electronic resource] /
_cedited by Cristina Davino, Francesco Palumbo, Adalbert F. X. Wilhelm, Hans A. Kestler.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aX, 153 p. 40 illus., 35 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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490 1 _aStudies in Classification, Data Analysis, and Knowledge Organization,
_x2198-3321
505 0 _aPreface -- Building hierarchies of factors with disjoint factor analysis -- Uncertainty in Latent Trait Models and dimensionality reduction methods for complex data: an analysis of taxpayer perception on the Fiscal System -- The predictivity of access tests for university success -- Asynchronous and synchronous-asynchronous particle swarms -- The impact of the Covid-19 pandemic on modelling volatility and risk analysis of returns in selected European financial markets -- Asymmetric binary regression models for imbalanced datasets: an application to students’ churn -- Computational models supporting decision-making in managing publication activity at Polish universities -- Stability of nonparametric methods for cognitive diagnostic assessment -- SMARTS: SeMi-supervised clustering for Assessment of Reviews using Topic and Sentiment -- The equitable and sustainable wellbeing through the pandemic. A first study to assess changes at local level in Italy -- Choice-Based Optimization under High-Dimensional MNL -- A first glance on co-evolution of Boolean networks to simulate the development of cross-talking systems in molecular biology -- Classification on polish fund market during COVID-19 pandemic - extreme risk modeling approach.
520 _aThis book collects together selected peer-reviewed contributions presented at the European Conference on Data Analysis, ECDA 2022, held in Naples, Italy, September 14-16, 2022. Highlighting the role of statistics in discovering novel and interesting patterns in the era of big data, it follows the motto of the conference: “Avoiding drowning in the data: recent trends and future challenges in learning from data”. The central focus is on multidisciplinary approaches to data analysis, classification, and the interface between computer science, data mining and statistics. Both methodological and applied topics are covered. The former includes supervised and unsupervised techniques with particular emphasis on advances in regression and clustering analysis and constructing composite indicators. The applications are mainly in risk analysis, biology, and education. The volume is organized into four main macro themes: methodological contributions in the social sciences and education, multivariate analysis methods for big data, innovative contributions for applications inspired by biology, and strategies for analyzing complex data in finance.
650 0 _aQuantitative research.
650 0 _aData mining.
_96941
650 0 _aMathematical statistics
_xData processing.
_964750
650 0 _aStatistics.
_965982
650 0 _aStatistics .
_965982
650 0 _aMachine learning.
_955059
650 1 4 _aData Analysis and Big Data.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aStatistics and Computing.
_966446
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aApplied Statistics.
_965986
650 2 4 _aStatistical Learning.
_967991
700 1 _aDavino, Cristina.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_982106
700 1 _aPalumbo, Francesco.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
_982107
700 1 _aWilhelm, Adalbert F. X.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
_982108
700 1 _aKestler, Hans A.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_982109
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031544675
776 0 8 _iPrinted edition:
_z9783031544699
830 0 _aStudies in Classification, Data Analysis, and Knowledge Organization,
_x2198-3321
_982110
856 4 0 _uhttps://doi.org/10.1007/978-3-031-54468-2
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