Cause Effect Pairs in Machine Learning / The Springer Series on Challenges in Machine Learning (PDF)
(Sprache: Englisch)
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,...
sofort als Download lieferbar
Printausgabe Fr. 130.00
eBook (pdf)
Fr. 106.50
inkl. MwSt.
- Kreditkarte, Paypal, Rechnung
- Kostenloser tolino webreader
Produktdetails
Produktinformationen zu „Cause Effect Pairs in Machine Learning / The Springer Series on Challenges in Machine Learning (PDF)“
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.
Bibliographische Angaben
- 2019, 1st ed. 2019, 372 Seiten, Englisch
- Herausgegeben: Isabelle Guyon, Alexander Statnikov, Berna Bakir Batu
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3030218104
- ISBN-13: 9783030218102
- Erscheinungsdatum: 22.10.2019
Abhängig von Bildschirmgrösse und eingestellter Schriftgrösse kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: PDF
- Grösse: 11 MB
- Ohne Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
Kommentar zu "Cause Effect Pairs in Machine Learning / The Springer Series on Challenges in Machine Learning"
0 Gebrauchte Artikel zu „Cause Effect Pairs in Machine Learning / The Springer Series on Challenges in Machine Learning“
| Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
|---|
Schreiben Sie einen Kommentar zu "Cause Effect Pairs in Machine Learning / The Springer Series on Challenges in Machine Learning".
Kommentar verfassen