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oapen-20.500.12657-860982023-12-13T11:31:10Z Classification and Data Science in the Digital Age Brito, Paula Dias, José G. Lausen, Berthold Montanari, Angela Nugent, Rebecca Classification Data Science Clustering Statistical Learning Machine Learning Data Analysis Mutlivariate Analysis Statistical Inference Dimension Reduction Functional Data Analysis Time Series Analysis Network Analysis bic Book Industry Communication::U Computing & information technology::UM Computer programming / software development::UMB Algorithms & data structures bic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence bic Book Industry Communication::U Computing & information technology::UF Business applications::UFM Mathematical & statistical software bic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning bic Book Industry Communication::U Computing & information technology::UN Databases::UNF Data mining bic Book Industry Communication::P Mathematics & science::PB Mathematics::PBT Probability & statistics The contributions gathered in this open access book focus on modern methods for data science and classification and present a series of real-world applications. Numerous research topics are covered, ranging from statistical inference and modeling to clustering and dimension reduction, from functional data analysis to time series analysis, and network analysis. The applications reflect new analyses in a variety of fields, including medicine, marketing, genetics, engineering, and education. The book comprises selected and peer-reviewed papers presented at the 17th Conference of the International Federation of Classification Societies (IFCS 2022), held in Porto, Portugal, July 19–23, 2022. The IFCS federates the classification societies and the IFCS biennial conference brings together researchers and stakeholders in the areas of Data Science, Classification, and Machine Learning. It provides a forum for presenting high-quality theoretical and applied works, and promoting and fostering interdisciplinary research and international cooperation. The intended audience is researchers and practitioners who seek the latest developments and applications in the field of data science and classification. 2023-12-13T10:35:47Z 2023-12-13T10:35:47Z 2023 book ONIX_20231213_9783031090349_4 9783031090349 9783031090332 https://library.oapen.org/handle/20.500.12657/86098 eng Studies in Classification, Data Analysis, and Knowledge Organization application/pdf n/a 978-3-031-09034-9.pdf https://link.springer.com/978-3-031-09034-9 Springer Nature Springer International Publishing 10.1007/978-3-031-09034-9 10.1007/978-3-031-09034-9 6c6992af-b843-4f46-859c-f6e9998e40d5 06217aaf-d965-40ef-a13f-f68d59c94b9a 9783031090349 9783031090332 Springer International Publishing 416 Cham [...] open access
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The contributions gathered in this open access book focus on modern methods for data science and classification and present a series of real-world applications. Numerous research topics are covered, ranging from statistical inference and modeling to clustering and dimension reduction, from functional data analysis to time series analysis, and network analysis. The applications reflect new analyses in a variety of fields, including medicine, marketing, genetics, engineering, and education. The book comprises selected and peer-reviewed papers presented at the 17th Conference of the International Federation of Classification Societies (IFCS 2022), held in Porto, Portugal, July 19–23, 2022. The IFCS federates the classification societies and the IFCS biennial conference brings together researchers and stakeholders in the areas of Data Science, Classification, and Machine Learning. It provides a forum for presenting high-quality theoretical and applied works, and promoting and fostering interdisciplinary research and international cooperation. The intended audience is researchers and practitioners who seek the latest developments and applications in the field of data science and classification.
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