Cause Effect Pairs in Machine Learning - Couverture rigide

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9783030218096: Cause Effect Pairs in Machine Learning

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ISBN 10 : 3030218090 ISBN 13 : 9783030218096

Editeur : Springer, 2019

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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.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Springer
Date d'�dition: 2019
Langue: anglais
ISBN 10: 3030218090
ISBN 13: 9783030218096
Reliure: Reli�
Nombre de pages: 388
Coordonn�es du fabricant: non disponible
Personne responsable: CMN Printpool Inh. Mathis Neumann
https://www.also.com/ec/cms5/en_6000/6000/index.jsp

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9783030218126: Cause Effect Pairs in Machine Learning

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Cause Effect Pairs in Machine Learning.

Guyon, Isabelle; et al. (Eds.)

Edit� par Cham, Springer., 2019

ISBN 10 : 3030218090 ISBN 13 : 9783030218096

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XVI, 372 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband besto�en, daher M�ngelexemplar 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. N� de r�f. du vendeur 9057GB

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Cause Effect Pairs in Machine Learning

Guyon, Isabelle|Statnikov, Alexander|Batu, Berna Bakir

Edit� par Springer International Publishing, 2019

ISBN 10 : 3030218090 ISBN 13 : 9783030218096

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Comprehensive&nbspreference for those interested in the cause-effect problem, and&nbsphow to tackle them using machine learning algorithmsIncludes&nbspsix tutorial chapters, beginning with the simplest cases and common methods, to alg. N� de r�f. du vendeur 332921122

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Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)

Guyon

Edit� par Springer, 2019

ISBN 10 : 3030218090 ISBN 13 : 9783030218096

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Cause Effect Pairs in Machine Learning

Isabelle Guyon

Edit� par Springer International Publishing, Springer Nature Switzerland, 2019

ISBN 10 : 3030218090 ISBN 13 : 9783030218096

Neuf Couverture rigide

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N� de r�f. du vendeur 9783030218096

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Cause Effect Pairs in Machine Learning

Isabelle Guyon

Edit� par Springer International Publishing, Springer Nature Switzerland Nov 2019, 2019

ISBN 10 : 3030218090 ISBN 13 : 9783030218096

Neuf Couverture rigide

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

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Buch. Etat : 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. N� de r�f. du vendeur 9783030218096

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Cause Effect Pairs in Machine Learning

Isabelle Guyon

Edit� par Springer International Publishing, Springer Nature Switzerland Nov 2019, 2019

ISBN 10 : 3030218090 ISBN 13 : 9783030218096

Neuf Couverture rigide

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Buch. Etat : Neu. 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 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 388 pp. Englisch. N� de r�f. du vendeur 9783030218096

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