EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 690 (October 2024) A&A, 690 (2024) A131 References
Open Access
Issue
A&A
Volume 690, October 2024
Article Number A131
Number of page(s) 22
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202346798
Published online 02 October 2024
  1. Aerts, C., Mathis, S., & Rogers, T. 2019, ARA&A, 57, 35 [Google Scholar]
  2. Angus, R., Beane, A., Price-Whelan, A. M., et al. 2020, AJ, 160, 90 [Google Scholar]
  3. Balona, L. A., & Abedigamba, O. P. 2016, MNRAS, 461, 497 [NASA ADS] [CrossRef] [Google Scholar]
  4. Barnes, S. A. 2003, ApJ, 586, 464 [Google Scholar]
  5. Baron, M. 2019, Probability and Statistics for Computer Scientists (Chapman and Hall/CRC) [CrossRef] [Google Scholar]
  6. Bicz, K., Falewicz, R., Pietras, M., Siarkowski, M., & Pres, P. 2022, ApJ, 935, 102 [NASA ADS] [CrossRef] [Google Scholar]
  7. Biehl, M., Bunte, K., Longo, G., & Rino, P. 2018, in ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ed. M. Verleysen, vol. 26 (Ciaco - i6doc.com), 307 [Google Scholar]
  8. Bonanno, A., & Corsaro, E. 2022, ApJ, 939, L26 [NASA ADS] [CrossRef] [Google Scholar]
  9. Blancato, K., Ness, M. K., Huber, D., Lu, Y. L., & Angus, R. 2022, ApJ, 933, 241 [NASA ADS] [CrossRef] [Google Scholar]
  10. Borucki, W., Koch, D., Batalha, N., et al. 2009, in Transiting Planets, 253, 289 [NASA ADS] [Google Scholar]
  11. Borucki, W. J., Koch, D., Basri, G., et al. 2010, Science, 327, 977 [Google Scholar]
  12. Bouvier, J., & Bertout, C. 1989, A&A, 211, 99 [NASA ADS] [Google Scholar]
  13. Breiman, L. 2001, Mach. LEARN., 45, 5 [NASA ADS] [CrossRef] [Google Scholar]
  14. Breton, S. N., Santos, A. R., Bugnet, L., et al. 2021, A&A, 647, A125 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  15. Brown, T. M., Latham, D. W., Everett, M. E., & Esquerdo, G. A. 2011, AJ, 142, 112 [Google Scholar]
  16. Brun, A. S., & Browning, M. K. 2017, Living Rev. Solar Phys., 14, 1 [NASA ADS] [CrossRef] [Google Scholar]
  17. Bugnet, L., García, R., Davies, G., et al. 2018, A&A, 620, A38 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Cantiello, M., & Braithwaite, J. 2011, A&A, 534, A140 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  19. Chen, T., & Guestrin, C. 2016, XGBoost: A Scalable Tree Boosting System [Google Scholar]
  20. Chen, T., Benesty, M., & He, T. 2018, Understand Your Dataset with Xgboost, https:cran.r-project.org/web/packages/xgboost/vignettes/discoverYourData.html, Accessed on 2022-09-18 [Google Scholar]
  21. Claytor, Z. R., van Saders, J. L., Llama, J., et al. 2022, ApJ, 927, 219 [NASA ADS] [CrossRef] [Google Scholar]
  22. Claytor, Z. R., Van Saders, J. L., Cao, L., et al. 2024, ApJ, 962, 47 [NASA ADS] [CrossRef] [Google Scholar]
  23. Damasso, M., Del Sordo, F., Anglada-Escudé, G., et al. 2020, Sci. Adv., 6, eaax7467 [CrossRef] [Google Scholar]
  24. Eggenberger, P., Miglio, A., Montalban, J., et al. 2009, Effects of rotation on the evolution and asteroseismic properties of red giants [Google Scholar]
  25. Friedman, J. H. 2001, Ann. Statist., 1189 [Google Scholar]
  26. García, R., Ceillier, T., Salabert, D., et al. 2014, A&A, 572, A34 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. Géron, A. 2017, Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems (O’Reilly Media, Inc.) [Google Scholar]
  28. Hall, O. J., Davies, G. R., van Saders, J., et al. 2021, Nat. Astron., 5, 707 [NASA ADS] [CrossRef] [Google Scholar]
  29. Hastie, T., Tibshirani, R., & Friedman, J. 2009, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. (Springer) [Google Scholar]
  30. Haykin, S. 2009, Neural Networks and Leraning Machines (PHI Learning), 944 [Google Scholar]
  31. Howell, S. B., Sobeck, C., Haas, M., et al. 2014, PASP, 126, 398 [Google Scholar]
  32. Huber, D., Bryson, S. T., Haas, M. R., et al. 2016, The K2 Ecliptic Plane Input Catalog (EPIC) and Stellar Classifications of 138,600 Targets in Campaigns 1-8 [Google Scholar]
  33. Hyndman, R. J., & Koehler, A. B. 2006, Int. J. Forecast., 22, 679 [CrossRef] [Google Scholar]
  34. James, G., Witten, D., Hastie, T., & Tibshirani, R. 2013, Springer Texts in Statistics, 103, An Introduction to Statistical Learning - with Applications in R (New York: Springer) [CrossRef] [Google Scholar]
  35. Kuhn, M., Johnson, K., et al. 2013, Applied Predictive Modeling, 26 (Springer) [CrossRef] [Google Scholar]
  36. Küker, M., Rüdiger, G., Olah, K., & Strassmeier, K. G. 2019, A&A, 622, A40 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  37. Lu, Y., Angus, R., Agüeros, M. A., et al. 2020, AJ, 160, 168 [Google Scholar]
  38. Marcos-Arenal, P., Zima, W., De Ridder, J., et al. 2014, A&A, 566, A92 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  39. Mathur, S., Huber, D., Batalha, N. M., et al. 2017, ApJS, 229, 18 [Google Scholar]
  40. McQuillan, A., Mazeh, T., & Aigrain, S. 2014, ApJS, 211, 24 [Google Scholar]
  41. Messina, S. 2021, A&A, 645, A144 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  42. Messina, S., Nardiello, D., Desidera, S., et al. 2022, A&A, 657, L3 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  43. Mullally, S. 2020, MAST Kepler Archive Manual [Google Scholar]
  44. Murphy, K. 2012, Machine Learning: A Probabilistic Perspective, Adaptive Computation and Machine Learning (MIT Press) [Google Scholar]
  45. Pichara Baksai, K. E., Protopapas, P., & Leon, D. 2016, ApJ, 819, 18 [NASA ADS] [CrossRef] [Google Scholar]
  46. Pontius, R. G., Thontteh, O., & Chen, H. 2008, Environ. Ecol. Statist., 15, 111 [NASA ADS] [CrossRef] [Google Scholar]
  47. R Core Team 2023, R: A Language and Environment for Statistical Computing (Vienna, Austria: R Foundation for Statistical Computing) [Google Scholar]
  48. Ramachandran, K. M., & Tsokos, C. P. 2020, Mathematical Statistics with Applications in R (Academic Press) [Google Scholar]
  49. Raschka, S., & Mirjalili, V. 2017, Python Machine Learning, 2nd edn. (Livery Place 35 Livery Street Birmingham B3 2PB, UK: Packt Publishing Ltd.) [Google Scholar]
  50. Ricker, G. R., Winn, J. N., Vanderspek, R., et al. 2014, SPIE Conf. Ser., 9143, 914320 [Google Scholar]
  51. Rosich, A., Herrero, E., Mallonn, M., et al. 2020, A&A, 641, A82 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  52. Santos, A. R. G., García, R. A., Mathur, S., et al. 2019, Surface rotation and photometric activity for Kepler targets I. M and K main-sequence stars [Google Scholar]
  53. Santos, A., Breton, S., Mathur, S., & García, R. 2021, ApJS, 255, 17 [NASA ADS] [CrossRef] [Google Scholar]
  54. Skumanich, A. 1972, ApJ, 171, 565 [Google Scholar]
  55. Strassmeier, K. G. 2009, A&AR, 17, 251 [CrossRef] [Google Scholar]
  56. Torgo, L. 2011, Data Mining with R: Learning with Case Studies (Chapman and Hall/CRC) [CrossRef] [Google Scholar]
  57. Van Saders, J. L., Ceillier, T., Metcalfe, T. S., et al. 2016, Nature, 529, 181 [NASA ADS] [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.