Free Access
Volume 546, October 2012
Article Number A13
Number of page(s) 8
Section Numerical methods and codes
Published online 28 September 2012
  1. Abdalla, F. B., Banerji, M., Lahav, O., & Rashkov, V. 2011, MNRAS, 417, 1891 [NASA ADS] [CrossRef] [Google Scholar]
  2. Albrecht, A., Bernstein, G., Cahn, R., et al. 2006, Report of the Dark Energy Task Force [arXiv:astro-ph/0609591] [Google Scholar]
  3. Bishop, C. M., Pattern Recognition and Machine Learning 2006 (Springer) [Google Scholar]
  4. Brescia, M., Cavuoti, S., D’Abrusco, R., Laurino, O., & Longo, G. 2011, V International Workshop on Distributed Cooperative Laboratories: Instrumenting the Grid, in Remote Instrumentation for eScience and Related Aspects, 2011, eds. F. Davoli, et al. (New York: Springer) [Google Scholar]
  5. Brescia, M., Cavuoti, S., Paolillo, M., Longo, G., & Puzia, T. 2012a, MNRAS, 421, 1155 [NASA ADS] [CrossRef] [Google Scholar]
  6. Brescia, M., Longo, G., Castellani, M., et al. 2012b, Mem. SAIt Suppl., 19, 324 [Google Scholar]
  7. Broyden, C. G. 1970, J. Inst. Math. Appl., 6, 76 [CrossRef] [Google Scholar]
  8. Byrd, R. H., Nocedal, J., & Schnabel, R. B. 1994, Math. Progr., 63, 129 [CrossRef] [Google Scholar]
  9. Capak, P., Cowie, L. L., Hu, E. M., et al. 2004, AJ, 127, 180 [NASA ADS] [CrossRef] [Google Scholar]
  10. Capozzi, D., De Filippis, E., Paolillo, M., D’Abrusco, R., & Longo, G. 2009, MNRAS, 396, 900 [NASA ADS] [CrossRef] [Google Scholar]
  11. Carliles, S., Budavári, T., Heinis, S., Priebe, C., & Szalay, A. S. 2010, ApJ, 712, 511 [NASA ADS] [CrossRef] [Google Scholar]
  12. Collister, A. A., & Lahav, O. 2004, PASP, 116, 345 [NASA ADS] [CrossRef] [Google Scholar]
  13. Cowie, L. L., Barger, A. J., Hu, E. M., Capak, P., & Songaila, A. 2004, AJ, 127, 3137 [NASA ADS] [CrossRef] [Google Scholar]
  14. Csabai, I., Budavári, T., Connolly, A. J., et al. 2003, AJ, 125, 580 [NASA ADS] [CrossRef] [Google Scholar]
  15. D’Abrusco, R., Staiano, A., Longo, G., et al. 2007, ApJ, 663, 752 [NASA ADS] [CrossRef] [Google Scholar]
  16. Davidon, W. C. 1968, Comput. J., 10, 406 [CrossRef] [Google Scholar]
  17. Euclid Red Book, ESA Technical Document, 2011, ESA/SRE(2011)12 [arXiv:astro-ph/1110.3193] [Google Scholar]
  18. Fletcher, R. 1970, Comp. J., 13, 317 [CrossRef] [Google Scholar]
  19. Giavalisco, M., Ferguson, H. C., Koekemoer, A. M., et al. 2004, ApJ, 600, L93 [NASA ADS] [CrossRef] [Google Scholar]
  20. Geisser, S. 1975, J. Am. Statist. Assoc., 70, 320 [CrossRef] [Google Scholar]
  21. Goldfarb, D. 1970, Math. Comput., 24, 23 [CrossRef] [Google Scholar]
  22. Hildebrandt, H., Wolf, C., & Benitez, N. 2008, A&A, 480, 703 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  23. Hildebrandt, H., Arnouts, S., Capak, P., Wolf, C., et al. 2010, A&A, 523, A31 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. Hogg, D. W., Cohen, J. G., Blandford, R., et al. 1998, ApJ, 115, 1418 [NASA ADS] [CrossRef] [Google Scholar]
  25. Huterer, D., Takada, M., Bernstein, G., & Jain, B. 2006, MNRAS 366, 101 [Google Scholar]
  26. Koo, D. C. 1999, ASP Conf. Ser., 191, 3, eds. Weymann, Storrie-Lombardi, Sawicki & Brunner [NASA ADS] [Google Scholar]
  27. Keiichi, U., Medezinski, E., Nonino, M., et al. 2012, ApJ, 755, 56 [NASA ADS] [CrossRef] [Google Scholar]
  28. Laurino, O., D’Abrusco, R., Longo, G., & Riccio, G. 2011, MNRAS, 418, 2165 [NASA ADS] [CrossRef] [Google Scholar]
  29. Le Févre, O., Vettolani, G., Paltani, S., et al. 2004, A&A, 428, 1043 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  30. Li, I. H., & Yee, H. K. C. 2008, AJ, 135, 809 [NASA ADS] [CrossRef] [Google Scholar]
  31. Massarotti, M., Iovino, A., & Buzzoni, A. 2001a, A&A, 368, 74 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  32. Massarotti, M., Iovino, A., Buzzoni, A., & Valls-Gabaud, D. 2001b, A&A, 380, 425 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  33. Mizutani, E., & Dreyfus, S. E. 2001, On complexity analysis of supervised MLP-learning for algorithmic comparisons. In Proceedings of the 14th INNS-IEEE International Joint Conference on Neural Networks (IJCNN) (Washington, DC, Jul.), 347, 352 [Google Scholar]
  34. Noll, S., Mehlert, D., Appenzeller, I., et al. 2004, A&A, 418, 885 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  35. Peacock, J. A., Schneider, P., Efstathiou, G., et al. 2006, ESA-ESO Working Group on Fundamental Cosmology, Tech. Rep. [Google Scholar]
  36. Reddy, N. A., Steidel, C. C., Erb, D. K., Shapley, A. E., & Pettini, M. 2006, ApJ, 653, 1004 [NASA ADS] [CrossRef] [Google Scholar]
  37. Shanno, D. F. 1970, Math. Comput., 24, 647 [CrossRef] [MathSciNet] [Google Scholar]
  38. Sylvain, A., & Celisse, A. 2010, A survey of cross-validation procedures for model selection, Statistics Surveys, 4, 40 [CrossRef] [Google Scholar]
  39. Treu, T., Ellis, R. S., Liao, T. X., & van Dokkum, P. G. 2005, ApJ, 633, 174 [NASA ADS] [CrossRef] [Google Scholar]
  40. Vashist, R., & Garg, M. L. 2012, A Rough Set Approach for Generation and Validation of Rules for Missing Attribute Values of a Data Set, IJCA (0975-8887), 42, 31, 35 [CrossRef] [Google Scholar]
  41. Wirth, G. D., Willmer, C. N. A., Amico, P., et al. 2004, AJ, 127, 3121 [NASA ADS] [CrossRef] [Google Scholar]
  42. Wolf, C. 2009, MNRAS, 397, 520 [NASA ADS] [CrossRef] [Google Scholar]

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