Zhixuan (20 Ergebnisse)

hardcover. Zustand: Very Good.

Zustand: New. In.

Paperback. Zustand: Brand New. 108 pages. 9.25x6.10x0.25 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores new frontiers in the research of economic growth and industrial reconstruction, analyzing economic growth and transitions in industrial structure in East Asia with a variety of data. First, the effects of demographic change on trade openness is analyzed empirically using the panel data of APEC countries. Second, the determinant of wage and housing costs are estimated using survey data collected from peasant workers in China. Third, the determinants of conquests among nomads in or near China and dynasties from world history are analyzed empirically using data regarding dynasties. Fourth, critiques on Emmanuel's unequal exchange theory are investigated based on the profit data in the world. This book is highly recommended for readers who would like to obtain a new idea about economic development in terms of industrial structure.…

Zustand: New. In.

Zustand: New. In.

Zustand: New.

Gebunden. Zustand: New.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book primarily focuses on constraints and solutions for energy and electric power development. On the basis of analyses, proposes a planning index system including 26 binding indicators and the breaking constraints measures. Offering significant insights and proposals concerning the current status of energy development and the key limiting factors to sustainable energy development in China, it is a valuable resource for policy-makers, managers and researchers in the energy sector.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book primarily focuses on constraints and solutions for energy and electric power development. On the basis of analyses, proposes a planning index system including 26 binding indicators and the breaking constraints measures. Offering significant insights and proposals concerning the current status of energy development and the key limiting factors to sustainable energy development in China, it is a valuable resource for policy-makers, managers and researchers in the energy sector.…

Hardcover. Zustand: Brand New. 325 pages. 9.25x6.10x0.87 inches. In Stock.

Taschenbuch. Zustand: Neu. Constraints and Solutions for Energy and Electricity Development | Zhixuan Wang | Taschenbuch | xii | Englisch | 2019 | Springer | EAN 9789811341168 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Zustand: New.

Zustand: New. In.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 316 | Sprache: Englisch | Produktart: Bücher | This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields.Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.…

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields.Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields.Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.…

Taschenbuch. Zustand: Neu. Machine Learning for Causal Inference | Sheng Li (u. a.) | Taschenbuch | xvi | Englisch | 2024 | Springer | EAN 9783031350535 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Hardcover. Zustand: Brand New. 314 pages. 9.25x6.10x9.21 inches. In Stock.

Hardcover. Zustand: gut. 2023. Machine Learning for Causal Inference In deutscher Sprache. pages.