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            Classification and Data Science in the Digital Age

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            Contributor(s)
            Brito, Paula (editor)
            Dias, José G. (editor)
            Lausen, Berthold (editor)
            Montanari, Angela (editor)
            Nugent, Rebecca (editor)
            Language
            English
            Show full item record
            Abstract
            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.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/194117
            Keywords
            Classification; Data Science; Clustering; Statistical Learning; Machine Learning; Data Analysis; Mutlivariate Analysis; Statistical Inference; Dimension Reduction; Functional Data Analysis; Time Series Analysis; Network Analysis
            DOI
            10.1007/978-3-031-09034-9
            ISBN
            9783031090349, 9783031090332
            Publisher
            Springer Nature
            Publisher website
            http://www.springernature.com/oabooks
            Publication date and place
            Cham, 2023
            Imprint
            Springer International Publishing
            Series
            Studies in Classification, Data Analysis, and Knowledge Organization,
            Classification
            Algorithms & data structures
            Artificial intelligence
            Mathematical & statistical software
            Machine learning
            Data mining
            Probability & statistics
            Pages
            416
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            Credits


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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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