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Titlebook: Advanced Studies in Classification and Data Science; Tadashi Imaizumi,Akinori Okada,Maurizio Vichi Conference proceedings 2020 Springer Na

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發(fā)表于 2025-3-21 18:19:32 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Advanced Studies in Classification and Data Science
影響因子2023Tadashi Imaizumi,Akinori Okada,Maurizio Vichi
視頻videohttp://file.papertrans.cn/147/146249/146249.mp4
發(fā)行地址Focuses on analysis of device data in data science.Applies methods and techniques of data science to marketing science, economics, and related fields.Builds an interface between statistical methods an
學(xué)科分類Studies in Classification, Data Analysis, and Knowledge Organization
圖書(shū)封面Titlebook: Advanced Studies in Classification and Data Science;  Tadashi Imaizumi,Akinori Okada,Maurizio Vichi Conference proceedings 2020 Springer Na
影響因子.This edited volume focuses on the latest developments in classification and data science and covers a wide range of topics in the context of data analysis and related areas, e.g. the analysis of complex data, analysis of qualitative data, methods for high-dimensional data, dimensionality reduction, data visualization, multivariate statistical methods, and various applications to real data in the social sciences, medical sciences, and other disciplines...In addition to sharing theoretical and methodological findings, the book shows how to apply the proposed methods to a variety of problems — e.g. in consumer behavior, decision-making, marketing data and social network structures. Both methodological aspects and applications to a wide range of areas such as economics, behavioral science, marketing science, management science and the social sciences are covered...The book is chiefly intended for researchers and practitioners who are interested in the latest developments and practical applications in these fields, as well as applied statisticians and data analysts. Its combination of methodological advances with a wide range of real-world applications gathered from several fields make
Pindex Conference proceedings 2020
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Genetic Markers of Extreme Human Longevityp of simulations and modeled according to this. That was the reason why we created an estimating model of weighted tendencies, in which the estimated values of the above-mentioned tendencies were defined as average estimated values by weighting the explanatory features of functions.
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https://doi.org/10.1007/978-3-030-24970-0aper we shall briefly review five possible strategies. Although we conceed that the purpose of this paper is not to provide a comprehensive examination of their utility—future investigations of their properties will confirm any further benefits in their use.
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Ryuta Nakae,Yasuo Murai,Shoji Yokobori tables induces a clustering algorithm for weighted networks, differing from spectral clustering or modularity maximization techniques. Also, we present a novel co-clustering algorithm, distinct from the Baum–Welch algorithm, applicable to HMM models for unrestricted bigrams counts. Three case studies illustrate the theory.
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Biomarkers of Radiation in the Environmentr approach on 442 germline samples that were sequenced on a customized diagnostic gene panel comprising exons of 49 genes and found that the proposed method outperforms existing approaches both in sensitivity and specificity.
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