Articoli correlati a Cause Effect Pairs in Machine Learning
Sinossi
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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Dalla quarta di copertina
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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
Compra nuovo
Visualizza questo articolo
EUR 124,20
EUR 9,70 per la spedizione da Germania a Italia
Destinazione, tempi e costi
Risultati della ricerca per Cause Effect Pairs in Machine Learning
Foto dell'editore
Cause Effect Pairs in Machine Learning.
Da: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germania
Valutazione del venditore 5 su 5 stelle
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. Codice articolo 9057GB
Quantità: 1 disponibili
Immagini fornite dal venditore
Cause Effect Pairs in Machine Learning
Editore:
Springer International Publishing, 2019
ISBN 10: 3030218090
ISBN 13: 9783030218096
Nuovo
Rilegato
Da: moluna, Greven, Germania
Valutazione del venditore 5 su 5 stelle
Condizione: 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. Codice articolo 332921122
Quantità: Più di 20 disponibili
Foto dell'editore
Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)
Da: Ria Christie Collections, Uxbridge, Regno Unito
Valutazione del venditore 5 su 5 stelle
Condizione: New. In. Codice articolo ria9783030218096_new
Quantità: Più di 20 disponibili
Immagini fornite dal venditore
Cause Effect Pairs in Machine Learning
ISBN 10: 3030218090
ISBN 13: 9783030218096
Nuovo
Rilegato
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Valutazione del venditore 5 su 5 stelle
Buch. Condizione: 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. Codice articolo 9783030218096
Quantità: 2 disponibili
Immagini fornite dal venditore
Cause Effect Pairs in Machine Learning
ISBN 10: 3030218090
ISBN 13: 9783030218096
Nuovo
Rilegato
Da: AHA-BUCH GmbH, Einbeck, Germania
Valutazione del venditore 5 su 5 stelle
Buch. Condizione: 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. Codice articolo 9783030218096
Quantità: 1 disponibili
Immagini fornite dal venditore
Cause Effect Pairs in Machine Learning
ISBN 10: 3030218090
ISBN 13: 9783030218096
Nuovo
Rilegato
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
Valutazione del venditore 5 su 5 stelle
Buch. Condizione: 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. Codice articolo 9783030218096
Quantità: 2 disponibili
Foto dell'editore
Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)
Da: California Books, Miami, FL, U.S.A.
Valutazione del venditore 5 su 5 stelle
Condizione: New. Codice articolo I-9783030218096
Quantità: Più di 20 disponibili
Foto dell'editore
Cause Effect Pairs in Machine Learning
Da: Books Puddle, New York, NY, U.S.A.
Valutazione del venditore 4 su 5 stelle
Condizione: New. pp. XVI, 372 122 illus., 90 illus. in color. 1 Edition NO-PA16APR2015-KAP. Codice articolo 26384553258
Quantità: 4 disponibili
Foto dell'editore
Cause Effect Pairs in Machine Learning
Print on DemandDa: Biblios, Frankfurt am main, HESSE, Germania
Valutazione del venditore 5 su 5 stelle
Condizione: New. PRINT ON DEMAND pp. XVI, 372 122 illus., 90 illus. in color. Codice articolo 18384553248
Quantità: 4 disponibili
Foto dell'editore
Cause Effect Pairs in Machine Learning
Print on DemandDa: Majestic Books, Hounslow, Regno Unito
Valutazione del venditore 5 su 5 stelle
Condizione: New. Print on Demand pp. XVI, 372 122 illus., 90 illus. in color. Codice articolo 379350773
Quantità: 4 disponibili