Training Gradient Boosting Machines Using Curve-Fitting and Information-Theoretic Features for Causal Direction Detection-Reference-Cited by-同舟云学术

Training Gradient Boosting Machines Using Curve-Fitting and Information-Theoretic Features for Causal Direction Detection

Published:2019 Issue: Volume: Page:331-338
ISSN:2520-131X
Container-title:Cause Effect Pairs in Machine Learning
language:
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Author:

Samothrakis Spyridon,Perez Diego,Lucas Simon

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-21810-2_11

Reference9 articles.

1. David Arthur and Sergei Vassilvitskii. k-means++: The advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pages 1027–1035. Society for Industrial and Applied Mathematics, 2007.

2. Andrea Falcon. Aristotle on causality. In Edward N. Zalta, editor, The Stanford Encyclopedia of Philosophy. Winter 2012 edition, 2012.

3. James George Frazer. The Golden Bough: A Study in Magic and Religion. Vol. 13, Aftermath: a Supplement to the Golden Bough. Macmillan, 1936.

4. Jerome H Friedman. Greedy function approximation: a gradient boosting machine. Annals of Statistics, pages 1189–1232, 2001.

5. Patrik O Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Schölkopf. Nonlinear causal discovery with additive noise models. 2009.

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