Supervised and Unsupervised Statistical Data Analysis
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, add...
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| Altres autors: | , , , , |
| Format: | Electrònic eBook |
| Idioma: | anglès |
| Publicat: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Edició: | 1st ed. 2025. |
| Col·lecció: | Studies in Classification, Data Analysis, and Knowledge Organization,
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| Matèries: | |
| Accés en línia: | https://doi.org/10.1007/978-3-032-03042-9 |
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| 245 | 1 | 0 | |a Supervised and Unsupervised Statistical Data Analysis |h [electronic resource] / |c edited by Antonio D'Ambrosio, Mark de Rooij, Kim De Roover, Carmela Iorio, Michele La Rocca. |
| 250 | |a 1st ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
| 300 | |a IX, 354 p. 76 illus., 59 illus. in color. |b online resource. | ||
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| 490 | 1 | |a Studies in Classification, Data Analysis, and Knowledge Organization, |x 2198-3321 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. Please note that a more accessible version of this eBook is available as ePub. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 650 | 0 | |a Machine learning. | |
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| 700 | 1 | |a D'Ambrosio, Antonio. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a de Rooij, Mark. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a De Roover, Kim. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Iorio, Carmela. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a La Rocca, Michele. |e editor. |0 (orcid)0000-0002-8768-4606 |1 https://orcid.org/0000-0002-8768-4606 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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