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Supervised and unsupervised statisti...

D'Ambrosio, Antonio.

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Supervised and unsupervised statistical data analysis

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Supervised and unsupervised statistical data analysis / edited by Antonio D'Ambrosio ... [et al.].
其他作者:	D'Ambrosio, Antonio.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	ix, 354 p. :ill. (some col.), digital ;24 cm.
內容註:	Measuring discrimination in decision making algorithms an approach based on causal inference -- Leveraging Social Network Analysis for Semantic Differential Scale: An application to Survey Data -- Extending the Boosted Oriented Probabilistic Clustering to the Unit Hypersphere A Textual Data Perspect -- Understanding ESG Scores Through Network Analysis A Study Using Graph Neural Networks -- Innovative applications of Supervised Learning in addressing missing Data a case study on social surveys -- ISP Index A Parsimonious Method to Predict Defaults.
Contained By:	Springer Nature eBook
標題:	Big data - Congresses. - Statistical methods -
電子資源:	https://doi.org/10.1007/978-3-032-03042-9
ISBN:	9783032030429

Supervised and unsupervised statistical data analysis
Supervised and unsupervised statistical data analysis [electronic resource] /edited by Antonio D'Ambrosio ... [et al.]. - Cham :Springer Nature Switzerland :2025. - ix, 354 p. :ill. (some col.), digital ;24 cm. - Studies in classification, data analysis, and knowledge organization,2198-3321. - Studies in classification, data analysis, and knowledge organization..

Measuring discrimination in decision making algorithms an approach based on causal inference -- Leveraging Social Network Analysis for Semantic Differential Scale: An application to Survey Data -- Extending the Boosted Oriented Probabilistic Clustering to the Unit Hypersphere A Textual Data Perspect -- Understanding ESG Scores Through Network Analysis A Study Using Graph Neural Networks -- Innovative applications of Supervised Learning in addressing missing Data a case study on social surveys -- ISP Index A Parsimonious Method to Predict Defaults.

The contributions in this book offer new insights into the theoretical and practical challenges of supervised and unsupervised learning, highlighting the remarkable breadth of contemporary statistical research while maintaining methodological rigor. Innovative approaches to statistical modeling, addressing spatial dependencies and circular data structures, are presented alongside significant advances in interpretable machine learning that reconcile statistical precision with algorithmic transparency. Particularly noteworthy is the volume's treatment of complex data structures, including novel methods for network analysis, high-dimensional clustering, temporal pattern recognition and optimization techniques. The volume interweaves methodological innovation and practical relevance, and the applications span diverse domains, including the social sciences and biomedical engineering, each demonstrating the effective translation of statistical theory into real-world impact. The book contains peer-reviewed contributions presented at the special edition of the 15th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, namely the International Scientific Joint Meeting of the Italian and Dutch-Flemish Classification Societies (CLADAG-VOC 2025), held in Naples, Italy, September 8-10, 2025. The conference provided fresh perspectives on the current state of research in clustering, classification and data analysis, and underpinned the value and significance of international collaboration, addressing the emerging needs of an increasingly complex data landscape and offering novel solutions to long-standing challenges in statistical data analysis.

ISBN: 9783032030429

Standard No.: 10.1007/978-3-032-03042-9doiSubjects--Topical Terms:

3663819
Big data
--Statistical methods--Congresses.

LC Class. No.: QA76.9.B45 / S73 2025

Dewey Class. No.: 005.7

Supervised and unsupervised statistical data analysis
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