Artículos relacionados a Cause Effect Pairs in Machine Learning (The Springer...
Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning) - Tapa blanda
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.
"Sinopsis" puede pertenecer a otra edición de este libro.
De la contraportada
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.
EUR 121,32
EUR 17,08 gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envíoComprar nuevo
Ver este artículo
EUR 99,24
EUR 5,14 gastos de envío desde Reino Unido a España
Destinos, gastos y plazos de envío
Resultados de la búsqueda para Cause Effect Pairs in Machine Learning (The Springer...
Imagen de archivo
Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In. Nº de ref. del artículo: ria9783030218126_new
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Cause Effect Pairs in Machine Learning
Publicado por
Springer International Publishing, 2020
ISBN 10: 3030218120
ISBN 13: 9783030218126
Nuevo
Kartoniert / Broschiert
Librería: moluna, Greven, Alemania
Calificación del vendedor: 4 de 5 estrellas
Kartoniert / Broschiert. 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: 448675598
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Cause Effect Pairs in Machine Learning
Publicado por
Springer International Publishing, Springer International Publishing Nov 2020, 2020
ISBN 10: 3030218120
ISBN 13: 9783030218126
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. 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: 9783030218126
Cantidad disponible: 2 disponibles
Imagen del vendedor
Cause Effect Pairs in Machine Learning
Publicado por
Springer International Publishing, Springer International Publishing, 2020
ISBN 10: 3030218120
ISBN 13: 9783030218126
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. 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. Nº de ref. del artículo: 9783030218126
Cantidad disponible: 1 disponibles
Imagen del vendedor
Cause Effect Pairs in Machine Learning
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 42322309-n
Cantidad disponible: 15 disponibles
Imagen de archivo
Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)
Librería: California Books, Miami, FL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: I-9783030218126
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
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: 26384558670
Cantidad disponible: 4 disponibles
Imagen de archivo
Cause Effect Pairs in Machine Learning
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Print on Demand pp. XVI, 372 122 illus., 90 illus. in color. Nº de ref. del artículo: 379345297
Cantidad disponible: 4 disponibles
Imagen del vendedor
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: 42322309
Cantidad disponible: 15 disponibles
Imagen del vendedor
Cause Effect Pairs in Machine Learning
Publicado por
Springer International Publishing, Springer International Publishing Nov 2020, 2020
ISBN 10: 3030218120
ISBN 13: 9783030218126
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783030218126
Cantidad disponible: 2 disponibles