Librería: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Alemania
EUR 13,00
Cantidad disponible: 1 disponibles
Añadir al carritoXVI, 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.
Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 139,90
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New. In.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 184,80
Cantidad disponible: 4 disponibles
Añadir al carritoCondición: New. pp. XVI, 372 122 illus., 90 illus. in color. 1 Edition NO-PA16APR2015-KAP.
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 186,94
Cantidad disponible: 15 disponibles
Añadir al carritoCondición: New.
Idioma: Inglés
Publicado por Springer International Publishing, Springer Nature Switzerland, 2019
ISBN 10: 3030218090 ISBN 13: 9783030218096
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 149,79
Cantidad disponible: 1 disponibles
Añadir al carritoBuch. Condición: 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.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 222,86
Cantidad disponible: 2 disponibles
Añadir al carritoHardcover. Condición: Brand New. 372 pages. 9.25x6.25x1.00 inches. In Stock.
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de America
EUR 234,80
Cantidad disponible: 15 disponibles
Añadir al carritoCondición: New.
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 118,26
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: new. Questo è un articolo print on demand.
Idioma: Inglés
Publicado por Springer International Publishing, Springer Nature Switzerland Nov 2019, 2019
ISBN 10: 3030218090 ISBN 13: 9783030218096
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 149,79
Cantidad disponible: 2 disponibles
Añadir al carritoBuch. 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.
Idioma: Inglés
Publicado por Springer International Publishing, 2019
ISBN 10: 3030218090 ISBN 13: 9783030218096
Librería: moluna, Greven, Alemania
EUR 124,20
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: 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.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 192,26
Cantidad disponible: 4 disponibles
Añadir al carritoCondición: New. Print on Demand pp. XVI, 372 122 illus., 90 illus. in color.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 191,09
Cantidad disponible: 4 disponibles
Añadir al carritoCondición: New. PRINT ON DEMAND pp. XVI, 372 122 illus., 90 illus. in color.
Idioma: Inglés
Publicado por Springer, Springer Nov 2019, 2019
ISBN 10: 3030218090 ISBN 13: 9783030218096
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 149,79
Cantidad disponible: 1 disponibles
Añadir al carritoBuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 388 pp. Englisch.