EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 664 (August 2022) A&A, 664 (2022) A38 References
Open Access
Issue
A&A
Volume 664, August 2022
Article Number A38
Number of page(s) 15
Section Catalogs and data
DOI https://doi.org/10.1051/0004-6361/202243130
Published online 04 August 2022
  1. Allende Prieto, C., Sivarani, T., Beers, T. C., et al. 2008, AJ, 136, 2070 [Google Scholar]
  2. Anguiano, B., Majewski, S. R., Allende-Prieto, C., et al. 2018, A&A, 620, A76 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  3. Awad, M., & Khanna, R. 2015, Support Vector Regression (Berkeley, CA: Apress), 67 [Google Scholar]
  4. Bai, Y., Liu, J., Wang, S., & Yang, F. 2018, AJ, 157, 9 [NASA ADS] [CrossRef] [Google Scholar]
  5. Bai, Y., Liu, J., Bai, Z., Wang, S., & Fan, D. 2019, AJ, 158, 93 [NASA ADS] [CrossRef] [Google Scholar]
  6. Bailer-Jones, C. A. L. 2011, MNRAS, 411, 435 [Google Scholar]
  7. Boeche, C., Smith, M. C., Grebel, E. K., et al. 2018, AJ, 155, 181 [NASA ADS] [CrossRef] [Google Scholar]
  8. Boser, B. E., Guyon, I. M., & Vapnik, V. N. 1992, in Proceedings of the Fifth AnnualWorkshop on Computational Learning Theory, COLT ’92 (New York, NY, USA: Association for Computing Machinery), 144 [CrossRef] [Google Scholar]
  9. Breiman, L. 2001, Statist. Sci., 16, 199 [CrossRef] [Google Scholar]
  10. Bu, Y., Kumar, Y. B., Xie, J., et al. 2020, ApJS, 249, 7 [NASA ADS] [CrossRef] [Google Scholar]
  11. Cenarro, A. J., Moles, M., Marín-Franch, A., et al. 2014, in Proc. SPIE, 9149, Observatory Operations: Strategies, Processes, and Systems V, 91491I [Google Scholar]
  12. Cenarro, A. J., Moles, M., Cristóbal-Hornillos, D., et al. 2019, A&A, 622, A176 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  13. Cortes, C., & Vapnik, V. 1995, Mach. Learn., 20, 273 [Google Scholar]
  14. Cover, T., & Hart, P. 1967, IEEE Trans. Inf. Theory, 13, 21 [CrossRef] [Google Scholar]
  15. Cristianini, N., & Shawe-Taylor, J. 2000, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (Cambridge University Press) [CrossRef] [Google Scholar]
  16. Deng, L.-C., Newberg, H. J., Liu, C., et al. 2012, Res. Astron. Astrophys., 12, 735 [Google Scholar]
  17. Drucker, H., Burges, C. J., Kaufman, L., et al. 1997, Adv. Neural Inform. Process. Syst., 9, 155 [Google Scholar]
  18. Galarza, C. A., Daflon, S., Placco, V. M., et al. 2022, A&A, 657, A35 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  19. García Pérez, A. E., Allende Prieto, C., Holtzman, J. A., et al. 2016, AJ, 151, 144 [Google Scholar]
  20. Jiménez-Teja, Y., Dupke, R. A., Lopes de Oliveira, R., et al. 2019, A&A, 622, A183 [Google Scholar]
  21. Jönsson, H., Prieto, C. A., Holtzman, J. A., et al. 2018, AJ, 156, 126 [CrossRef] [Google Scholar]
  22. Lee, Y. S., Beers, T. C., Sivarani, T., et al. 2008a, AJ, 136, 2022 [Google Scholar]
  23. Lee, Y. S., Beers, T. C., Sivarani, T., et al. 2008b, AJ, 136, 2050 [Google Scholar]
  24. Lilliefors, H. W. 1967, J. Am. Statist. Assoc., 62, 399 [CrossRef] [Google Scholar]
  25. Liu, C., Fu, J., Shi, J., Wu, H., & Zuo, F. 2020 Eprint arXiv [arXiv:2005.07210] [Google Scholar]
  26. Lu, Y., & Li, X. 2015, MNRAS, 452, 1394 [CrossRef] [Google Scholar]
  27. MacKay, D. J. 2003, Information Theory, Inference and Learning Algorithms (Cambridge University Press) [Google Scholar]
  28. Marín-Franch, A., Taylor, K., Cenarro, J., Cristobal-Hornillos, D., & Moles, M. 2015, in IAU General Assembly, 29, 2257381 [Google Scholar]
  29. Nogueira-Cavalcante, J. P., Dupke, R., Coelho, P., et al. 2019, A&A, 630, A88 [EDP Sciences] [Google Scholar]
  30. Quinlan, J. R. 1986, Mach. Learn., 1, 81 [Google Scholar]
  31. Ruder, S. 2017, An overview of gradient descent optimization algorithms [arXiv:1609.04747] [Google Scholar]
  32. Shalev-Shwartz, S., & Ben-David, S. 2014, Understanding Machine Learning: From Theory to Algorithms (Cambridge University Press) [CrossRef] [Google Scholar]
  33. Sichevskij, S. G. 2012, Astron. Rep., 56, 710 [NASA ADS] [CrossRef] [Google Scholar]
  34. Sichevskij, S. G., Mironov, A. V., & Malkov, O. Y. 2014, Astrophys. Bull., 69, 160 [NASA ADS] [CrossRef] [Google Scholar]
  35. Smola, A. J., & Schölkopf, B. 2004, Statist. Comput., 14, 199 [CrossRef] [Google Scholar]
  36. Steinmetz, M., Matijevič, G., Enke, H., et al. 2020, AJ, 160, 82 [NASA ADS] [CrossRef] [Google Scholar]
  37. Stone, C. J. 1977, Ann. Statist., 5, 595 [CrossRef] [Google Scholar]
  38. Taylor, M. B. 2005, in Astronomical Society of the Pacific Conference Series, Vol. 347, Astronomical Data Analysis Software and Systems XIV, eds. P. Shopbell, M. Britton, & R. Ebert, 29 [NASA ADS] [Google Scholar]
  39. Wang, J., Fu, J.-N., Zong, W., et al. 2020, ApJS, 251, 27 [NASA ADS] [CrossRef] [Google Scholar]
  40. Wang, C., Bai, Y., Yuan, H., Wang, S., & Liu, J. 2021, A&A, 649, A144 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  41. Whitten, D. D., Placco, V. M., Beers, T. C., et al. 2019, A&A, 622, A182 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  42. Wu, Y., Luo, A. L., Li, H.-N., et al. 2011, Res. Astron. Astrophys., 11, 924 [Google Scholar]
  43. Wu, Y., Luo, A., Du, B., Zhao, Y., & Yuan, H. 2014, Automatic stellar spectral parameterization pipeline for LAMOST survey, Proceedings of IAU Symposium, 306 [Google Scholar]
  44. Yang, L., Yuan, H., Xiang, M., et al. 2022, A&A, 659, A181 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  45. Yanny, B., Rockosi, C., Newberg, H. J., et al. 2009, AJ, 137, 4377 [Google Scholar]
  46. Yuan, H., Liu, X., Xiang, M., et al. 2015, ApJ, 799, 133 [NASA ADS] [CrossRef] [Google Scholar]
  47. Zasowski, G., Johnson, J. A., Frinchaboy, P. M., et al. 2013, AJ, 146, 81 [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.