Cause Effect Pairs in Machine Learning

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Guyon, Isabelle (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Statnikov, Alexander (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Batu, Berna Bakir (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: The Springer Series on Challenges in Machine Learning,
• Θέματα: Artificial intelligence.; Optical data processing.; Pattern recognition.; Artificial Intelligence.; Image Processing and Computer Vision.; Pattern Recognition.

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 learnin...