Cause Effect Pairs in Machine Learning - Couverture souple

9783030218126: Cause Effect Pairs in Machine Learning

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ISBN 10 : 3030218120 ISBN 13 : 9783030218126

Editeur : Springer, 2020

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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: 2020
Langue: anglais
ISBN 10: 3030218120
ISBN 13: 9783030218126
Reliure: Broch�
Nombre de pages: 388
�diteur: Guyon Isabelle, Statnikov Alexander, Batu Berna Bakir
Coordonn�es du fabricant: non disponible
Personne responsable: CMN Printpool Inh. Mathis Neumann
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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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Cause Effect Pairs in Machine Learning

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

Edit� par Springer International Publishing, 2020

ISBN 10 : 3030218120 ISBN 13 : 9783030218126

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Kartoniert / Broschiert. 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 448675598

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

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ISBN 10 : 3030218120 ISBN 13 : 9783030218126

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Cause Effect Pairs in Machine Learning

Isabelle Guyon

Edit� par Springer International Publishing, Springer International Publishing, 2020

ISBN 10 : 3030218120 ISBN 13 : 9783030218126

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Taschenbuch. 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 9783030218126

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Cause Effect Pairs in Machine Learning

Isabelle Guyon

Edit� par Springer International Publishing, Springer International Publishing Nov 2020, 2020

ISBN 10 : 3030218120 ISBN 13 : 9783030218126

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Taschenbuch. 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 9783030218126

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Cause Effect Pairs in Machine Learning

Isabelle Guyon

Edit� par Springer International Publishing, Springer International Publishing Nov 2020, 2020

ISBN 10 : 3030218120 ISBN 13 : 9783030218126

Neuf Taschenbuch

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. 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 9783030218126

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