000 | 04284nam a22005775i 4500 | ||
---|---|---|---|
001 | 978-3-030-21810-2 | ||
003 | DE-He213 | ||
005 | 20240423130141.0 | ||
007 | cr nn 008mamaa | ||
008 | 191022s2019 sz | s |||| 0|eng d | ||
020 |
_a9783030218102 _9978-3-030-21810-2 |
||
024 | 7 |
_a10.1007/978-3-030-21810-2 _2doi |
|
050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
|
072 | 7 |
_aCOM004000 _2bisacsh |
|
072 | 7 |
_aUYQ _2thema |
|
082 | 0 | 4 |
_a006.3 _223 |
245 | 1 | 0 |
_aCause Effect Pairs in Machine Learning _h[electronic resource] / _cedited by Isabelle Guyon, Alexander Statnikov, Berna Bakir Batu. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
|
300 |
_aXVI, 372 p. 122 illus., 90 illus. in color. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
||
337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
||
347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aThe Springer Series on Challenges in Machine Learning, _x2520-1328 |
|
505 | 0 | _a1. 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. | |
520 | _aThis 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. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aComputer vision. | |
650 | 0 | _aPattern recognition systems. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aComputer Vision. |
650 | 2 | 4 | _aAutomated Pattern Recognition. |
700 | 1 |
_aGuyon, Isabelle. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aStatnikov, Alexander. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aBatu, Berna Bakir. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030218096 |
776 | 0 | 8 |
_iPrinted edition: _z9783030218119 |
776 | 0 | 8 |
_iPrinted edition: _z9783030218126 |
830 | 0 |
_aThe Springer Series on Challenges in Machine Learning, _x2520-1328 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-21810-2 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c185592 _d185592 |