Free Access
Issue
A&A
Volume 611, March 2018
Article Number A97
Number of page(s) 11
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/201731106
Published online 10 April 2018
  1. Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., et al. 2009, ApJS, 182, 543 [NASA ADS] [CrossRef] [Google Scholar]
  2. Baum, W. A. 1962, in Problems of Extra-Galactic Research, ed. G. C. McVittie, IAU Symp., 15, 390 [NASA ADS] [Google Scholar]
  3. Blake, C., Collister, A., Bridle, S., & Lahav, O. 2007, MNRAS, 374, 1527 [NASA ADS] [CrossRef] [Google Scholar]
  4. Blomme, J., Sarro, L. M., O’Donovan, F. T., et al. 2011, MNRAS, 418, 96 [NASA ADS] [CrossRef] [Google Scholar]
  5. Bolzonella, M., Miralles, J.-M., & Pelló, R. 2000, A&A, 363, 476 [NASA ADS] [Google Scholar]
  6. Collister, A. A. & Lahav, O. 2004, PASP, 116, 345 [NASA ADS] [CrossRef] [Google Scholar]
  7. Cortes, C. & Vapnik, V. 1995, Mach. Learn., 20, 273 [Google Scholar]
  8. Coupon, J., Ilbert, O., Kilbinger, M., et al. 2009, A&A, 500, 981 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  9. Cristiani, S., Trentini, S., La Franca, F., & Andreani, P. 1997, A&A, 321, 123 [NASA ADS] [Google Scholar]
  10. Croom, S. M., Richards, G. T., Shanks, T., et al. 2009, MNRAS, 392, 19 [NASA ADS] [CrossRef] [Google Scholar]
  11. Dubath, P., Rimoldini, L., Süveges, M., et al. 2011, MNRAS, 414, 2602 [NASA ADS] [CrossRef] [Google Scholar]
  12. Duda, R. O. & Hart, P. E. 1973, Pattern classification and scene analysis (J. Wiley & Sons) [Google Scholar]
  13. Eyer, L. & Blake, C. 2005, MNRAS, 358, 30 [NASA ADS] [CrossRef] [Google Scholar]
  14. Firth, A. E., Lahav, O., & Somerville, R. S. 2003, MNRAS, 339, 1195 [NASA ADS] [CrossRef] [Google Scholar]
  15. Frieman, J. A., Bassett, B., Becker, A., et al. 2008, AJ, 135, 338 [NASA ADS] [CrossRef] [Google Scholar]
  16. Giveon, U., Maoz, D., Kaspi, S., Netzer, H., & Smith, P. S. 1999, MNRAS, 306, 637 [NASA ADS] [CrossRef] [Google Scholar]
  17. Han, B., Ding, H.-P., Zhang, Y.-X., & Zhao, Y.-H. 2016, Res. Astron. Astrophys., 16, 074 [NASA ADS] [Google Scholar]
  18. He, K., Zhang, X., Ren, S., & Sun, J. 2015, in Proc. IEEE Int. Conf. Computer Vision (ICCV), ICCV ’15 , 1026 [Google Scholar]
  19. Hernitschek, N., Schlafly, E. F., Sesar, B., et al. 2016, ApJ, 817, 73 [NASA ADS] [CrossRef] [Google Scholar]
  20. Hopkins, P. F., Hernquist, L., Cox, T. J., et al. 2006, ApJS, 163, 1 [NASA ADS] [CrossRef] [Google Scholar]
  21. Huertas-Company, M., Gravet, R., Cabrera-Vives, G., et al. 2015, ApJS, 221, 8 [NASA ADS] [CrossRef] [Google Scholar]
  22. Ilbert, O., Salvato, M., Le Floc’h, E., et al. 2010, ApJ, 709, 644 [NASA ADS] [CrossRef] [Google Scholar]
  23. Ioffe, S., & Szegedy, C. 2015, in Proceedings of the 32nd International Conference on Machine Learning (ICML-15), eds. D. Blei & F. Bach (JMLR Workshop and Conference Proceedings), 448 [Google Scholar]
  24. Ivezić, Ž., Smith, J. A., Miknaitis, G., et al. 2007, AJ, 134, 973 [NASA ADS] [CrossRef] [Google Scholar]
  25. Jia, Y., Shelhamer, E., Donahue, J., et al. 2014, ArXiv e-prints [arXiv:1408.5093] [Google Scholar]
  26. Joly, A., Goëau, H., Glotin, H., et al. 2016, in LifeCLEF 2016: Multimedia Life Species Identification Challenges, eds. N. Fuhr, P. Quaresma, T. Gonçalves, et al. (Cham: Springer International Publishing), 286 [Google Scholar]
  27. Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012, in Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3, Lake Tahoe, Nevada, United States, 1106 [Google Scholar]
  28. Kügler, S. D., Polsterer, K., & Hoecker, M. 2015, A&A, 576, A132 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  29. Le Guennec, A., Malinowski, S., & Tavenard, R. 2016, in ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, Riva Del Garda, Italy [Google Scholar]
  30. Lopez, S., Barrientos, L. F., Lira, P., et al. 2008, ApJ, 679, 1144 [NASA ADS] [CrossRef] [Google Scholar]
  31. LSST Science Collaboration 2009, ArXiv e-prints [arXiv:0912.0201] [Google Scholar]
  32. Meusinger, H., Hinze, A., & de Hoon, A. 2011, A&A, 525, A37 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  33. Nair, V., & Hinton, G. E. 2010, in Proceedings of the 27th International Conference on Machine Learning (ICML-10), eds. J. Fürnkranz & T. Joachims (Omnipress), 807 [Google Scholar]
  34. Nun, I., Protopapas, P., Sim, B., et al. 2015, ArXiv e-prints [arXiv:1506.00010] [Google Scholar]
  35. Oyaizu, H., Lima, M., Cunha, C. E., et al. 2008, ApJ, 674, 768 [NASA ADS] [CrossRef] [Google Scholar]
  36. Peng, N., Zhang, Y., Zhao, Y., & Wu, X.-b. 2012, MNRAS, 425, 2599 [NASA ADS] [CrossRef] [Google Scholar]
  37. Peters, C. M., Richards, G. T., Myers, A. D., et al. 2015, ApJ, 811, 95 [NASA ADS] [CrossRef] [Google Scholar]
  38. Portinari, L., Kotilainen, J., Falomo, R., & Decarli, R. 2012, MNRAS, 420, 732 [NASA ADS] [CrossRef] [Google Scholar]
  39. Quinlan, J. R. 1986, Mach. Learn., 1, 81 [Google Scholar]
  40. Rimoldini, L., Dubath, P., Süveges, M., et al. 2012, MNRAS, 427, 2917 [NASA ADS] [CrossRef] [Google Scholar]
  41. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. 1986, in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, (Cambridge, MA, USA: MIT Press), 318 [Google Scholar]
  42. Russakovsky, O., Deng, J., Su, H., et al. 2015, Int. J. Comput. Vis. (IJCV), 115, 211 [CrossRef] [Google Scholar]
  43. Schneider, D. P., Richards, G. T., Hall, P. B., et al. 2010, AJ, 139, 2360 [NASA ADS] [CrossRef] [Google Scholar]
  44. Sesar, B., Ivezić, Ž., Lupton, R. H., et al. 2007, AJ, 134, 2236 [NASA ADS] [CrossRef] [Google Scholar]
  45. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. 2014, J. Mach. Learn. Res., 15, 1929 [Google Scholar]
  46. Szegedy, C., Liu, W., Jia, Y., et al. 2015, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1 [Google Scholar]
  47. The Dark Energy Survey Collaboration 2005, ArXiv e-prints [arXiv:astro-ph/0510346] [Google Scholar]
  48. Vanden Berk, D. E., Wilhite, B. C., Kron, R. G., et al. 2004, ApJ, 601, 692 [NASA ADS] [CrossRef] [Google Scholar]
  49. Yèche, C., Petitjean, P., Rich, J., et al. 2010, A&A, 523, A14 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  50. York, D. G., Adelman, J., Anderson, Jr. J. E., et al. 2000, AJ, 120, 1579 [NASA ADS] [CrossRef] [Google Scholar]
  51. Zhang, Y., Li, L., & Zhao, Y. 2009, MNRAS, 392, 233 [NASA ADS] [CrossRef] [Google Scholar]
  52. Zhang, Y., Ma, H., Peng, N., Zhao, Y., & Wu, X.-b. 2013, AJ, 146, 22 [NASA ADS] [CrossRef] [Google Scholar]

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