Free Access
Volume 550, February 2013
Article Number A44
Number of page(s) 16
Section Astronomical instrumentation
Published online 23 January 2013
  1. Allard, F., Hauschildt, P. H., Alexander, D. R., Tamanai, A., & Schweitzer, A. 2001, ApJ, 556, 357 [NASA ADS] [CrossRef] [Google Scholar]
  2. Allard, F., Homeier, D., & Freytag, B. 2012, Roy. Soc. London Philos. Trans. Ser. A, 370, 2765 [NASA ADS] [CrossRef] [Google Scholar]
  3. Bailer-Jones, C. A. L., Smith, K. W., Tiede, C., Sordo, R., & Vallenari, A. 2008, MNRAS, 391, 1838 [NASA ADS] [CrossRef] [Google Scholar]
  4. Bayo, A., Barrado, D., Stauffer, J., et al. 2011, A&A, 536, A63 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Bishop, C. M. 2006, Pattern Recognition and Machine Learning (Information Science and Statistics) (Secaucus, NJ, USA: Springer-Verlag New York, Inc.) [Google Scholar]
  6. Blomme, R., Frémat, Y., Lobel, A., & Martayan, C. 2011, in EAS Publ. Ser., 45, 373 [Google Scholar]
  7. Burgasser, A. J., Kirkpatrick, J. D., Reid, I. N., et al. 2000, AJ, 120, 473 [NASA ADS] [CrossRef] [Google Scholar]
  8. Caballero, J. A., Burgasser, A. J., & Klement, R. 2008, A&A, 488, 181 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  9. Chiu, K., Fan, X., Leggett, S. K., et al. 2006, AJ, 131, 2722 [NASA ADS] [CrossRef] [Google Scholar]
  10. Coifman, R., & Lafon, S. 2006, Appl. Comput. Harm. Anal., 21, 5 [CrossRef] [Google Scholar]
  11. Cortes, C., & Vapnik, V. 1995, Machine Learning, 20, 273, 10.1007/ BF00994018 [Google Scholar]
  12. Cover, T. M., & Hart, P. E. 1967, IEEE Trans. Inf. Theory, 13, 21 [NASA ADS] [CrossRef] [Google Scholar]
  13. Cushing, M. C., Rayner, J. T., & Vacca, W. D. 2005, ApJ, 623, 1115 [NASA ADS] [CrossRef] [Google Scholar]
  14. de Bruijne, J. H. J. 2009, Gaia astrometric performance: summer-2009 status, ESA/ESTEC, Tech. rep. [Google Scholar]
  15. de Bruijne, J. H. J. 2012, Ap&SS, 341, 31 [NASA ADS] [CrossRef] [Google Scholar]
  16. Delfosse, X., Tinney, C. G., Forveille, T., et al. 1997, A&A, 327, L25 [NASA ADS] [Google Scholar]
  17. Gizis, J. E., Monet, D. G., Reid, I. N., Kirkpatrick, J. D., & Burgasser, A. J. 2000a, MNRAS, 311, 385 [NASA ADS] [CrossRef] [Google Scholar]
  18. Gizis, J. E., Monet, D. G., Reid, I. N., et al. 2000b, AJ, 120, 1085 [NASA ADS] [CrossRef] [Google Scholar]
  19. Golimowski, D. A., Leggett, S. K., Marley, M. S., et al. 2004, AJ, 127, 3516 [NASA ADS] [CrossRef] [Google Scholar]
  20. Hastie, T., Tibshirani, R., & Friedman, J. H. 2001, The elements of statistical learning: data mining, inference, and prediction: with 200 full-color illustrations (New York: Springer-Verlag), 533 [Google Scholar]
  21. Jordi, C., Gebran, M., Carrasco, J. M., et al. 2010, A&A, 523, A48 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  22. Kirkpatrick, J. D., Reid, I. N., Liebert, J., et al. 1999, ApJ, 519, 802 [NASA ADS] [CrossRef] [Google Scholar]
  23. Kirkpatrick, J. D., Reid, I. N., Liebert, J., et al. 2000, AJ, 120, 447 [NASA ADS] [CrossRef] [Google Scholar]
  24. Kirkpatrick, J. D., Cushing, M. C., Gelino, C. R., et al. 2011, ApJS, 197, 19 [NASA ADS] [CrossRef] [Google Scholar]
  25. Knapp, G. R., Leggett, S. K., Fan, X., et al. 2004, AJ, 127, 3553 [NASA ADS] [CrossRef] [Google Scholar]
  26. Lindegren, L., Lammers, U., Hobbs, D., et al. 2012, A&A, 538, A78 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. Liu, C., Bailer-Jones, C. A. L., Sordo, R., et al. 2012, MNRAS, 426, 2463 [NASA ADS] [CrossRef] [Google Scholar]
  28. McLean, I. S., McGovern, M. R., Burgasser, A. J., et al. 2003, ApJ, 596, 561 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  29. Mignard, F., Bailer-Jones, C., Bastian, U., et al. 2008, in IAU Symp. 248, eds. W. J. Jin, I. Platais, & M. A. C. Perryman, 224 [Google Scholar]
  30. Pearson, K. 1901, Philos. Mag., 2, 559 [CrossRef] [Google Scholar]
  31. Rasmussen, C., & Williams, C. 2006, Gaussian processes for machine learning, Adaptive computation and machine learning (MIT Press) [Google Scholar]
  32. Rayner, J. T., Cushing, M. C., & Vacca, W. D. 2009, ApJS, 185, 289 [NASA ADS] [CrossRef] [Google Scholar]
  33. Recio-Blanco, A., Bijaoui, A., & de Laverny, P. 2006, MNRAS, 370, 141 [NASA ADS] [CrossRef] [Google Scholar]
  34. Reid, I. N., Kirkpatrick, J. D., Gizis, J. E., & Liebert, J. 1999, ApJ, 527, L105 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  35. Reid, I. N., Kirkpatrick, J. D., Gizis, J. E., et al. 2000, AJ, 119, 369 [NASA ADS] [CrossRef] [Google Scholar]
  36. Reylé, C., Delorme, P., Willott, C. J., et al. 2010, A&A, 522, A112 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  37. Reylé, C., Rajpurohit, A. S., Schultheis, M., & Allard, F. 2011, in Stellar Systems, and the Sun, eds. C. Johns-Krull, M. K. Browning, & A. A. West, ASP Conf. Ser., 448, 16th Cambridge Workshop on Cool Stars, 929 [Google Scholar]
  38. Robin, A. C., Luri, X., Reylé, C., et al. 2012, A&A, 543, A100 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  39. Rosipal, R., Be, P. P., Trejo, L. J., et al. 2001, J. Mach. Learn. Res., 2, 97 [Google Scholar]
  40. Saumon, D., & Marley, M. S. 2008, ApJ, 689, 1327 [NASA ADS] [CrossRef] [Google Scholar]
  41. Shaw, J., Bridges, M., & Hobson, M. 2007, MNRAS, 378, 1365 [NASA ADS] [CrossRef] [Google Scholar]
  42. Sivia, D., & Skilling, J. 2006, Data analysis: a Bayesian tutorial, Oxford science publications (Oxford University Press) [Google Scholar]
  43. Skilling, J. 2006, Bayesian Anal., 1, 833 [CrossRef] [MathSciNet] [Google Scholar]
  44. Stephens, D. C., Leggett, S. K., Cushing, M. C., et al. 2009, ApJ, 702, 154 [NASA ADS] [CrossRef] [Google Scholar]
  45. Strauss, M. A., Fan, X., Gunn, J. E., et al. 1999, ApJ, 522, L61 [NASA ADS] [CrossRef] [Google Scholar]
  46. Tsalmantza, P., Karampelas, A., Kontizas, M., et al. 2012, A&A, 537, A42 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  47. Tsvetanov, Z. I., Golimowski, D. A., Zheng, W., et al. 2000, ApJ, 531, L61 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  48. Vapnik, V. N. 1995, The nature of statistical learning theory (New York, NY, USA: Springer-Verlag New York, Inc.) [Google Scholar]

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