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Sinopsis
Today, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence.
Since each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems.
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Acerca del autor
Yong Shi is the Director of the Research Center on Fictitious Economy and Data Science, and Director of the Key Lab of Big Data Mining and Knowledge Management, Chinese Academy of Sciences. He has been an Isaacson Professor, Union Pacific Chair, and Charles W. and Margre H. Durham Distinguished Professor of Information Technology at the College of Information Science and Technology, University of Nebraska at Omaha, USA. He has served on the State Council of the PRC (2016), as an elected member of the International Eurasian Academy of Science (2017), and as an elected fellow of the World Academy of Sciences for the Advancement of Science in Developing Countries (2015). His research interests include big data analysis, data science, business intelligence, data mining and multiple-criteria decision making. He has published more than 20 books, over 500 papers in various journals, and numerous conferences/proceedings papers. He is the Editor-in-Chief of both the International Journal ofInformation Technology and Decision Making (SCI) and of Annals of Data Science (Springer), and serves on the Editorial Boards of numerous academic journals.
De la contraportada
Today, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence.
Since each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems.
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- EditorialSpringer
- Año de publicación2022
- ISBN 10 9811636060
- ISBN 13 9789811636066
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de edición1
- Número de páginas744
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Today, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence.Since each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems. 744 pp. Englisch. Nº de ref. del artículo: 9789811636066
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Today, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence.Since each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems. Nº de ref. del artículo: 9789811636066
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