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Titlebook: Artificial Intelligence, Big Data and Data Science in Statistics; Challenges and Solut Ansgar Steland,Kwok-Leung Tsui Book 2022 The Editor(

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發(fā)表于 2025-3-21 17:43:39 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Artificial Intelligence, Big Data and Data Science in Statistics
期刊簡稱Challenges and Solut
影響因子2023Ansgar Steland,Kwok-Leung Tsui
視頻videohttp://file.papertrans.cn/163/162542/162542.mp4
發(fā)行地址Demonstrates the interplay between statistics, data science, machine learning and artificial intelligence.Focuses on applications in environmental science, the natural sciences, and technology.Feature
圖書封面Titlebook: Artificial Intelligence, Big Data and Data Science in Statistics; Challenges and Solut Ansgar Steland,Kwok-Leung Tsui Book 2022 The Editor(
影響因子This book discusses the interplay between statistics, data science, machine learning and artificial intelligence, with a focus on environmental science, the natural sciences, and technology. It covers the state of the art from both a theoretical and a practical viewpoint and describes how to successfully apply machine learning methods, demonstrating the benefits of statistics for modeling and analyzing high-dimensional and big data. The book’s expert contributions include theoretical studies of machine learning methods, expositions of general methodologies for sound statistical analyses of data as well as novel approaches to modeling and analyzing data for specific problems and areas. In terms of applications, the contributions deal with data as arising in industrial quality control, autonomous driving, transportation and traffic, chip manufacturing, photovoltaics, football, transmission of infectious diseases, Covid-19 and public health. The book will appeal to statisticians and datascientists, as well as engineers and computer scientists working in related fields or applications.
Pindex Book 2022
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978-3-031-07157-7The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerl
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Stratigraphy and Sedimentology,ed computing resources and for green machine learning. This especially applies when equipping mobile devices (sensors) with weak artificial intelligence. Results are discussed about supervised learning with such networks and regression methods in terms of consistency and bounds for the generalizatio
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https://doi.org/10.1007/b109876opment of a “hit” in music streaming data, with a rapid increase of the number of streams, to a peak, and a slow decay. With this application in mind, the method is scale invariant in the time domain as well as for the values of the time series (e.g., number of streams). Moreover, it is suitable als
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The Dating of Akortiri Aetokremnos,l piecewise sequential procedure is developed for estimating the mean of a normal population having an unknown variance. With the help of such fine-tuning, asymptotic unbiasedness of the terminal sample size can be achieved along with the added operational efficiency as a result of utilizing the . o
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Faunal Extinction in an Island Societyut and output spaces. In particular, neither moment conditions on the conditional distribution of .? given .?=?. nor the boundedness of the output space is needed. We obtain results on the existence and boundedness of the influence function and show qualitative robustness of the kernel-based estimat
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