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Sinopsis
This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect ("Does altitude cause a change in atmospheric pressure, or vice versa?") is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a "causal mechanism", in the sense that the values of one variable may have been generated from the values of the other.
This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website.
Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.
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This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other.
This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website.
Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Cause Effect Pairs in Machine Learning.
Publicado por
Cham, Springer., 2019
ISBN 10: 3030218090
ISBN 13: 9783030218096
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Librería: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Alemania
Calificación del vendedor: 5 de 5 estrellas
XVI, 372 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. The Springer Series on Challenges in Machine Learning. Sprache: Englisch. Nº de ref. del artículo: 9057GB
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Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)
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Condición: New. Nº de ref. del artículo: ABLIING23Mar3113020010122
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Cause Effect Pairs in Machine Learning
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Condición: New. Nº de ref. del artículo: 35757879-n
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Cause Effect Pairs in Machine Learning (Hardcover)
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Springer Nature Switzerland AG, Cham, 2019
ISBN 10: 3030218090
ISBN 13: 9783030218096
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Librería: Grand Eagle Retail, Mason, OH, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: new. Hardcover. This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (Does altitude cause a change in atmospheric pressure, or vice versa?) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a causal mechanism, in the sense that the values of one variable may have been generated from the values of the other. This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website.Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9783030218096
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Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Condición: New. In. Nº de ref. del artículo: ria9783030218096_new
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Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)
Librería: California Books, Miami, FL, Estados Unidos de America
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Cause Effect Pairs in Machine Learning
Publicado por
Springer International Publishing, Springer Nature Switzerland Nov 2019, 2019
ISBN 10: 3030218090
ISBN 13: 9783030218096
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents ground-breaking advances in the domain of causal structure learning.The problem of distinguishing cause from effect('Does altitude cause a change in atmospheric pressure, or vice versa ') is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of theChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a 'causal mechanism', in the sense that the values of one variable may have been generated from the values of the other.This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website.Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences. 388 pp. Englisch. Nº de ref. del artículo: 9783030218096
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Cause Effect Pairs in Machine Learning
Publicado por
Springer International Publishing, 2019
ISBN 10: 3030218090
ISBN 13: 9783030218096
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Librería: moluna, Greven, Alemania
Calificación del vendedor: 4 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Comprehensive reference for those interested in the cause-effect problem, and how to tackle them using machine learning algorithmsIncludes six tutorial chapters, beginning with the simplest cases and common methods, to alg. Nº de ref. del artículo: 332921122
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Cause Effect Pairs in Machine Learning
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 35757879
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Cause Effect Pairs in Machine Learning
Librería: Books Puddle, New York, NY, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Condición: New. pp. XVI, 372 122 illus., 90 illus. in color. 1 Edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26384553258
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