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...

Πίνακας περιεχομένων:

• 1. The cause-effect problem: motivation, ideas, and popular misconceptions
• 2. Evaluation methods of cause-effect pairs
• 3. Learning Bivariate Functional Causal Models
• 4. Discriminant Learning Machines
• 5. Cause-Effect Pairs in Time Series with a Focus on Econometrics
• 6. Beyond cause-effect pairs
• 7. Results of the Cause-Effect Pair Challenge
• 8. Non-linear Causal Inference using Gaussianity Measures
• 9. From Dependency to Causality: A Machine Learning Approach
• 10. Pattern-based Causal Feature Extraction
• 11. Training Gradient Boosting Machines using Curve-fitting and Information-theoretic Features for Causal Direction Detection
• 12. Conditional distribution variability measures for causality detection
• 13. Feature importance in causal inference for numerical and categorical variables
• 14. Markov Blanket Ranking using Kernel-based Conditional Dependence Measures.