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Titlebook: Classification and Data Science in the Digital Age; Paula Brito,José G. Dias,Rebecca Nugent Conference proceedings‘‘‘‘‘‘‘‘ 2023 The Editor

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書目名稱Classification and Data Science in the Digital Age
編輯Paula Brito,José G. Dias,Rebecca Nugent
視頻videohttp://file.papertrans.cn/228/227198/227198.mp4
概述Presents the latest advances in data science, classification, and machine learning.Covers methodological approaches to data science problems and real-world applications in different areas.Benefits res
叢書名稱Studies in Classification, Data Analysis, and Knowledge Organization
圖書封面Titlebook: Classification and Data Science in the Digital Age;  Paula Brito,José G. Dias,Rebecca Nugent Conference proceedings‘‘‘‘‘‘‘‘ 2023 The Editor
描述.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 classi
出版日期Conference proceedings‘‘‘‘‘‘‘‘ 2023
關(guān)鍵詞Classification; Data Science; Clustering; Statistical Learning; Machine Learning; Data Analysis; Mutlivari
版次1
doihttps://doi.org/10.1007/978-3-031-09034-9
isbn_softcover978-3-031-09033-2
isbn_ebook978-3-031-09034-9Series ISSN 1431-8814 Series E-ISSN 2198-3321
issn_series 1431-8814
copyrightThe Editor(s) (if applicable) and The Author(s). 2023
The information of publication is updating

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A New Regression Model for the Analysis of Microbiome Data,ead discrete distribution to analyze this kind of data is the Dirichletmultinomial. Despite its popularity, this distribution often fails in modeling microbiome data due to the strict parameterization imposed on its covariance matrix. The aim of this work is to propose a new distribution for analyzi
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Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendatiolable on online stores, it can become increasingly difficult for customers to identify. products that both satisfy their needs and represent the best deals overall. In this paper, we present a grocery recommender system based on the use of traditional machine learning methods aiming at assisting cus
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