Free Access
Volume 592, August 2016
Article Number A25
Number of page(s) 18
Section Numerical methods and codes
Published online 06 July 2016
  1. Abe, S., & Inoue, T. 2002, in European Symposium on Artificial Neural Networks, 113 [Google Scholar]
  2. Ahn, C. P., Alexandroff, R., Allen de Prieto, C., et al. 2014, ApJS, 211, 17 [NASA ADS] [CrossRef] [Google Scholar]
  3. Akbani, R., Kwek, S., & Japkowicz, N. 2004, in Proc. of the 15th European Conference on Machine Learning (ECML), 39 [Google Scholar]
  4. Alam, S., Albareti, F. D., Allen de Prieto, C., et al. 2015, ApJS, 219, 12 [NASA ADS] [CrossRef] [Google Scholar]
  5. Anderson, L. D., Bania, T. M., Balser, D. S., et al. 2014, ApJS, 212, 1 [NASA ADS] [CrossRef] [Google Scholar]
  6. Assef, R. J., Stern, D., Kochanek, C. S., et al. 2013, ApJ, 772, 26 [NASA ADS] [CrossRef] [Google Scholar]
  7. Beaumont, C. N., Williams, J. P., & Goodman, A. A. 2011, ApJ, 741, 14 [NASA ADS] [CrossRef] [Google Scholar]
  8. Bilicki, M., Jarrett, T. H., Peacock, J. A., Cluver, M. E., & Steward, L. 2014, ApJS, 210, 9 [NASA ADS] [CrossRef] [Google Scholar]
  9. Bilicki, M., Peacock, J. A., Jarrett, T. H., et al. 2016, ApJS, in press [Google Scholar]
  10. Bolton, A. S., Schlegel, D. J., Aubourg, É., et al. 2012, AJ, 144, 144 [NASA ADS] [CrossRef] [Google Scholar]
  11. Brown, M. J. I., Jarrett, T. H., & Cluver, M. E. 2014a, PASA, 31, 49 [NASA ADS] [CrossRef] [Google Scholar]
  12. Brown, M. J. I., Moustakas, J., Smith, J.-D. T., et al. 2014b, ApJS, 212, 18 [NASA ADS] [CrossRef] [Google Scholar]
  13. Bu, Y., Chen, F., & Pan, J. 2014, New A, 28, 35 [NASA ADS] [CrossRef] [Google Scholar]
  14. Cavuoti, S., Brescia, M., D’Abrusco, R., Longo, G., & Paolillo, M. 2014, MNRAS, 437, 968 [NASA ADS] [CrossRef] [Google Scholar]
  15. Chang, C.-C., & Lin, C.-J. 2011, ACM Trans. Intell. Syst. Technol., 2, 27:1 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  16. Cherkassky, V., & Mulier, F. 2006, Learning from Data: Concepts, Theory, and Methods, Second Edition (Wiley Online Library) [Google Scholar]
  17. Cluver, M. E., Jarrett, T. H., Hopkins, A. M., et al. 2014, ApJ, 782, 90 [NASA ADS] [CrossRef] [Google Scholar]
  18. Cristianini, N., & Shawe-Taylor, J. 2000, An introduction to Support Vector Machines (Cambridge University Press) [Google Scholar]
  19. Cutri, R. M., Wright, E. L., Conrow, T., et al. 2013, Explanatory Supplement to the AllWISE Data Release Products, Tech. rep. [Google Scholar]
  20. Dimitriadou, E., Hornik, K., Leisch, F., Meyer, D., & Weingessel, A. 2005, e1071: Misc Functions of the Department of Statistics (e1071), TU Wien, Version 1.5-11 [Google Scholar]
  21. Driver, S. P., Hill, D. T., Kelvin, L. S., et al. 2011, MNRAS, 413, 971 [NASA ADS] [CrossRef] [Google Scholar]
  22. Edelson, R., & Malkan, M. 2012, ApJ, 751, 52 [NASA ADS] [CrossRef] [Google Scholar]
  23. Eisenstein, D. J., Weinberg, D. H., Agol, E., et al. 2011, AJ, 142, 72 [NASA ADS] [CrossRef] [Google Scholar]
  24. Faherty, J. K., Alatalo, K., Anderson, L. D., et al. 2015, ArXiv e-prints [arXiv:1505.01923] [Google Scholar]
  25. Fan Wu, T., Lin, C.-J., & Weng, R. C. 2003, J. Machine Learning Research, 5, 975 [Google Scholar]
  26. Ferraro, S., Sherwin, B. D., & Spergel, D. N. 2015, Phys. Rev. D, 91, 083533 [NASA ADS] [CrossRef] [Google Scholar]
  27. Górski, K. M., Hivon, E., Banday, A. J., et al. 2005, ApJ, 622, 759 [NASA ADS] [CrossRef] [Google Scholar]
  28. Hambly, N. C., MacGillivray, H. T., Read, M. A., et al. 2001, MNRAS, 326, 1279 [NASA ADS] [CrossRef] [Google Scholar]
  29. Hsu, C.-W., Chang, C.-C., & Lin, C.-J. 2003, Bioinformatics, 1, 1 [Google Scholar]
  30. Ivezić, Ž., Monet, D. G., Bond, N., et al. 2008, in IAU Symp. 248, eds. W. J. Jin, I. Platais, & M. A. C. Perryman, 537 [Google Scholar]
  31. Jarrett, T. H., Chester, T., Cutri, R., et al. 2000, AJ, 119, 2498 [NASA ADS] [CrossRef] [Google Scholar]
  32. Jarrett, T. H., Cohen, M., Masci, F., et al. 2011, ApJ, 735, 112 [NASA ADS] [CrossRef] [Google Scholar]
  33. Jarrett, T. H., Cluver, M. E., Magoulas, C., et al. 2016, ApJ, submitted [Google Scholar]
  34. Kirkpatrick, J. D., Schneider, A., Fajardo-Acosta, S., et al. 2014, ApJ, 783, 122 [NASA ADS] [CrossRef] [Google Scholar]
  35. Klir, G. J., & Yuan, B. 1995, Fuzzy Sets and Fuzzy Logic: Theory and Applications (Upper Saddle River, NJ, USA: Prentice-Hall, Inc.) [Google Scholar]
  36. Kovács, A., & Szapudi, I. 2015, MNRAS, 448, 1305 [NASA ADS] [CrossRef] [Google Scholar]
  37. Lin, H.-T., Lin, C.-J., & Weng, R. C. 2007, Mach. Learn., 68, 267 [CrossRef] [Google Scholar]
  38. Mainzer, A., Bauer, J., Cutri, R. M., et al. 2014, ApJ, 792, 30 [NASA ADS] [CrossRef] [Google Scholar]
  39. Małek, K., Solarz, A., Pollo, A., et al. 2013, A&A, 557, A16 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  40. Mateos, S., Alonso-Herrero, A., Carrera, F. J., et al. 2012, MNRAS, 426, 3271 [NASA ADS] [CrossRef] [Google Scholar]
  41. Murakami, H., Baba, H., Barthel, P., et al. 2007, PASJ, 59, 369 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  42. Neugebauer, G., Habing, H. J., van Duinen, R., et al. 1984, ApJ, 278, L1 [NASA ADS] [CrossRef] [Google Scholar]
  43. Nikutta, R., Hunt-Walker, N., Nenkova, M., Ivezić, Ž., & Elitzur, M. 2014, MNRAS, 442, 3361 [NASA ADS] [CrossRef] [Google Scholar]
  44. Perryman, M. A. C., de Boer, K. S., Gilmore, G., et al. 2001, A&A, 369, 339 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  45. Platt, J. C. 1999, in Advances in large Margin Classifiers (MIT Press), 61 [Google Scholar]
  46. R Development Core Team 2005, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria [Google Scholar]
  47. Saglia, R. P., Tonry, J. L., Bender, R., et al. 2012, ApJ, 746, 128 [NASA ADS] [CrossRef] [Google Scholar]
  48. Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525 [NASA ADS] [CrossRef] [Google Scholar]
  49. Secrest, N. J., Dudik, R. P., Dorland, B. N., et al. 2015, ApJS, 221, 12 [NASA ADS] [CrossRef] [Google Scholar]
  50. Shawe-Taylor, S., & Cristianini, N. 2004, Kernel Methods for Pattern Analysis (Cambridge, UK: Cambridge, UP) [Google Scholar]
  51. Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163 [NASA ADS] [CrossRef] [Google Scholar]
  52. Solarz, A., Pollo, A., Takeuchi, T. T., et al. 2012, A&A, 541, A50 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  53. Soumagnac, M. T., Abdalla, F. B., Lahav, O., et al. 2015, MNRAS, 450, 666 [NASA ADS] [CrossRef] [Google Scholar]
  54. Stern, D., Assef, R. J., Benford, D. J., et al. 2012, ApJ, 753, 30 [NASA ADS] [CrossRef] [Google Scholar]
  55. Taylor, M. B. 2005, in Astronomical Data Analysis Software and Systems XIV, eds. P. Shopbell, M. Britton, & R. Ebert, ASP Conf. Ser., 347, 29 [Google Scholar]
  56. Taylor, M. B. 2006, in Astronomical Data Analysis Software and Systems XV, eds. C. Gabriel, C. Arviset, D. Ponz, & S. Enrique, ASP Conf. Ser., 351, 666 [Google Scholar]
  57. Tsujinishi, D., & Abe, S. 2003, Neural Networks, 16, 785 [CrossRef] [Google Scholar]
  58. Tu, X., & Wang, Z.-X. 2013, RA&A, 13, 323 [Google Scholar]
  59. Vapnik, V. 1999, IEEE Transactions on Neural Networks, 10, 988 [CrossRef] [PubMed] [Google Scholar]
  60. Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868 [NASA ADS] [CrossRef] [Google Scholar]
  61. Wu, X.-B., Hao, G., Jia, Z., Zhang, Y., & Peng, N. 2012, AJ, 144, 49 [NASA ADS] [CrossRef] [Google Scholar]
  62. Yan, L., Donoso, E., Tsai, C.-W., et al. 2013, AJ, 145, 55 [NASA ADS] [CrossRef] [Google Scholar]
  63. York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579 [NASA ADS] [CrossRef] [Google Scholar]

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