Open Access
Issue
A&A
Volume 680, December 2023
Article Number A109
Number of page(s) 16
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202347576
Published online 18 December 2023
  1. Abadi, M., Agarwal, A., Barham, P., et al. 2016, ArXiv e-prints [arXiv: 1603.04467] [Google Scholar]
  2. Ball, N. M., Brunner, R. J., Myers, A. D., & Tcheng, D. 2006, ApJ, 650, 497 [NASA ADS] [CrossRef] [Google Scholar]
  3. Bartoszek, K. 2016, J. Theor. Biol., 407, 371 [NASA ADS] [CrossRef] [Google Scholar]
  4. Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Bilicki, M., Dvornik, A., Hoekstra, H., et al. 2021, A&A, 653, A82 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Bloemen, S., Groot, P., Woudt, P., et al. 2016, Proc. SPIE, 9906, 990664 [NASA ADS] [CrossRef] [Google Scholar]
  7. Blum, R. D., Burleigh, K., Dey, A., et al. 2016, Am. Astron. Soc. Meeting Abstracts, 228, 317.01 [Google Scholar]
  8. Breiman, L. 2017, Classification and Regression Trees (Routledge) [CrossRef] [Google Scholar]
  9. Brier, G. W. 1950, Monthly Weather Rev., 78, 1 [CrossRef] [Google Scholar]
  10. Cabayol, L., Sevilla-Noarbe, I., Fernández, E., et al. 2018, MNRAS, 483, 529 [Google Scholar]
  11. Chen, T., & Guestrin, C. 2016, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining [Google Scholar]
  12. Cox, D. R. 1958, J. Roy. Stat. Soc. Ser. B (Methodological), 20, 215 [Google Scholar]
  13. Cui, Y., Jia, M., Lin, T.-Y., Song, Y., & Belongie, S. 2019, ArXiv e-prints [arXiv:1901.05555] [Google Scholar]
  14. Davis, J., & Goadrich, M. 2006, in Proceedings of the 23rd International Conference on Machine Learning, 233 [CrossRef] [Google Scholar]
  15. Degroot, M. H., & Fienberg, S. E. 1983, The Statistician, 32, 12 [CrossRef] [Google Scholar]
  16. Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168 [Google Scholar]
  17. Driver, S. P., Allen, P. D., Graham, A. W., et al. 2006, MNRAS, 368, 414 [Google Scholar]
  18. Fadely, R., Hogg, D. W., & Willman, B. 2012, ApJ, 760, 15 [CrossRef] [Google Scholar]
  19. Filho, T. S., Song, H., Perello-Nieto, M., et al. 2023, Mach. Learn., 112, 3211 [CrossRef] [Google Scholar]
  20. Fukushima, K., & Miyake, S. 1982, Pattern Recogn., 15, 455 [NASA ADS] [CrossRef] [Google Scholar]
  21. Groot, P. J., Bloemen, S., Vreeswijk, P. M., et al. 2022, SPIE Conf. Ser., 12182, 121821V [NASA ADS] [Google Scholar]
  22. Henrion, M., Mortlock, D. J., Hand, D. J., & Gandy, A. 2011, MNRAS, 412, 2286 [NASA ADS] [CrossRef] [Google Scholar]
  23. Heymans, C., Van Waerbeke, L., Miller, L., et al. 2012, MNRAS, 427, 146 [Google Scholar]
  24. Hosenie, Z., Bloemen, S., Groot, P. J., et al. 2021, Exp. Astron., 51, 319 [CrossRef] [Google Scholar]
  25. Jonas, J., & MeerKAT Team. 2016, in MeerKAT Science: On the Pathway to the SKA, 1 [Google Scholar]
  26. Kim, E. J., & Brunner, R. J. 2016, MNRAS, 464, 4463 [Google Scholar]
  27. Kingma, D. P., & Ba, J. 2015, in Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015) [Google Scholar]
  28. Kull, M., Filho, T. M. S., & Flach, P. 2017, Electron. J. Stat., 11, 5052 [CrossRef] [Google Scholar]
  29. Kull, M., Perello-Nieto, M., Kängsepp, M., et al. 2019, ArXiv e-prints [arXiv:1910.12656] [Google Scholar]
  30. LeCun, Y., Bengio, Y., et al. 1995, The Handbook of Brain Theory and Neural Networks, 3361, 1995 [Google Scholar]
  31. Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. 2017, in 2017 IEEE International Conference on Computer Vision (ICCV), 2999 [CrossRef] [Google Scholar]
  32. López-Sanjuan, C., Vázquez Ramió, H., Varela, J., et al. 2019, A&A, 622, A177 [Google Scholar]
  33. Maaten, L., & Hinton, G. 2008, J. Mach. Learn. Res., 9, 2579 [Google Scholar]
  34. McInnes, L., Healy, J., Saul, N., & Großberger, L. 2018, J. Open Source Softw., 3, 861 [CrossRef] [Google Scholar]
  35. Niculescu-Mizil, A., & Caruana, R. 2005, Proceedings of the 22nd International Conference on Machine Learning [Google Scholar]
  36. Odewahn, S. C., Stockwell, E. B., Pennington, R. L., Humphreys, R. M., & Zumach, W. A. 1992, AJ, 103, 318 [NASA ADS] [CrossRef] [Google Scholar]
  37. Odewahn, S. C., de Carvalho, R. R., Gal, R. R., et al. 2004, AJ, 128, 3092 [NASA ADS] [CrossRef] [Google Scholar]
  38. Paturel, G., Bottinelli, L., & Gouguenheim, L. 1995, Astrophys. Lett. Commun., 31, 13 [NASA ADS] [Google Scholar]
  39. Platt, J. 1999, in Advances in Large Margin Classifiers (MIT Press) [Google Scholar]
  40. Schlegel, D., Dey, A., Herrera, D., et al. 2021, Am. Astron. Soc. Meeting Abstracts, 53, 235.03 [NASA ADS] [Google Scholar]
  41. Sevilla-Noarbe, I., Hoyle, B., Marchã, M. J., et al. 2018, MNRAS, 481, 5451 [Google Scholar]
  42. Silva, D. R., Blum, R. D., Allen, L., et al. 2016, Am. Astron. Soc. Meeting Abstracts, 228, 317.02 [NASA ADS] [Google Scholar]
  43. Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163 [NASA ADS] [CrossRef] [Google Scholar]
  44. Stoppa, F., Vreeswijk, P., Bloemen, S., et al. 2022, A&A, 662, A109 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  45. Stoppa, F., Ruiz de Austri, R., Vreeswijk, P., et al. 2023, A&A, 680, A108 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Strateva, I., Ivezic, Ž., Knapp, G. R., et al. 2001, AJ, 122, 1861 [CrossRef] [Google Scholar]
  47. Swets, J. A. 1996, in Signal Detection Theory and ROC Analysis in Psychology and Diagnostics: Collected Papers (Psychology Press) [Google Scholar]
  48. Vasconcellos, E. C., de Carvalho, R. R., Gal, R. R., et al. 2011, AJ, 141, 189 [Google Scholar]
  49. Weir, N., Fayyad, U. M., & Djorgovski, S. 1995, AJ, 109, 2401 [NASA ADS] [CrossRef] [Google Scholar]
  50. Wenger, M., Ochsenbein, F., Egret, D., et al. 2000, A&AS, 143, 9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  51. Wu, Y., Liu, L., Bae, J., et al. 2019, ArXiv e-prints [arXiv: 1908.06477] [Google Scholar]
  52. Wyder, T. K., Martin, D. C., Schiminovich, D., et al. 2007, ApJS, 173, 293 [Google Scholar]
  53. Zou, H., Zhou, X., Fan, X., et al. 2019, ApJS, 245, 4 [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.