Free Access
Volume 611, March 2018
Article Number A97
Number of page(s) 11
Section Numerical methods and codes
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]

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.