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Home All issues Volume 423 / No 2 (August IV 2004) A&A, 423 2 (2004) 761-776 References
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Issue
A&A
Volume 423, Number 2, August IV 2004
Page(s) 761 - 776
Section Astronomical instrumentation
DOI https://doi.org/10.1051/0004-6361:20040176
Published online 06 August 2004
  1. Abazajian, K., Adelman Mc Carthy, J. K., Agüeros, M. A., et al. 2003, AJ, 126, 2081 [Google Scholar]
  2. Arnouts, S., Cristiani, S., Moscardini, L., et al. 1999, MNRAS, 310, 540 [NASA ADS] [CrossRef] [Google Scholar]
  3. Bailer-Jones, C. A. L., Gupta, R., & Singh, H. P. 2001, An introduction to artificial neural networks Proc. of the Workshop on Automated Data Analysis in Astronomy, IUCAA, Pune, India, October 9-12, 2000 [arXiv:astro-ph/0102224] [Google Scholar]
  4. Ball, N. M., Loveday, M., Fukugita, M., et al. 2004, MNARS, 348, 1038 [Google Scholar]
  5. Battiti, R., & Tecchiolli, G. 1995, Training neural nets with the reactive tabu search, IEEE Transactions on Neural Networks, 6(5):1185-1200 [Google Scholar]
  6. Benítez, N. 2000, ApJ, 536, 571 [NASA ADS] [CrossRef] [Google Scholar]
  7. Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  8. Bertsekas, D. P. 1995, Nonlinear Programming, Belmont, MA: Athena Scientific, ISBN 1-886529-14-0 [Google Scholar]
  9. Bishop, C. M. 1995, Neural networks for pattern recognition (Oxford University Press) [Google Scholar]
  10. Calzetti, D. 1997, in The ultraviolet universe at low and high redshift: Probing the progress of Galaxy evolution, ed. W. H. Waller et al., AIP Conf. Proc. 408 (Woodbury: AIP), 403 [Google Scholar]
  11. Cohen, J. G., Hogg, D. W., Blandford, R., et al. 2000, ApJ, 538, 29 [NASA ADS] [CrossRef] [Google Scholar]
  12. Coleman, G. D., Wu, C.-C., & Weedman, D. W. 1980, ApJS, 43, 393 [NASA ADS] [CrossRef] [Google Scholar]
  13. Connolly, A. J., Csabai, I., Szalay, A. S., et al. 1995, AJ, 110, 2655 [NASA ADS] [CrossRef] [Google Scholar]
  14. Cristiani, S., Appenzeller, I., Arnouts, S., et al. 2000, A&A, 359, 489 [NASA ADS] [Google Scholar]
  15. Cristiani, S., Renzini, A., & Williams, R. 2000, Deep Fields, Proc. of the ESO Workshop 9-12 Oct., ed. S. Cristiani, A. Renzini, & R. Williams (Springer Verlag) [Google Scholar]
  16. Csabai, I., Budvàri, T., Connolly, A. J., et al. 2003, AJ, 125, 580 [NASA ADS] [CrossRef] [Google Scholar]
  17. Dawson, S., Stern, D., Bunker, A. J., Spinrad, H., & Dey, A. 2001, AJ, 122, 598 [NASA ADS] [CrossRef] [Google Scholar]
  18. Fernández-Soto, A., Lanzetta, K. M., & Yahil, A. 1999, ApJ, 513, 34 [NASA ADS] [CrossRef] [Google Scholar]
  19. Fernández-Soto, A., Lanzetta, K. M., Chen, H. W., Pascarelle, S. M., & Yahata, N. 2001, ApJ, 135, 41 [Google Scholar]
  20. Fioc, M., & Rocca-Volmerange, B. 1997, A&A, 326, 950 [NASA ADS] [Google Scholar]
  21. Firth, A. E., Lahav, O., & Somerville, R. S. 2003, MNRAS, 339, 1195 [NASA ADS] [CrossRef] [Google Scholar]
  22. Fontana, A., Menci, N., D'Odorico, S., et al. 1999, MNRAS, 310, 27 [Google Scholar]
  23. Fontana, A., D'Odorico, S., Poli, F., et al. 2000, AJ, 120, 2206 [NASA ADS] [CrossRef] [Google Scholar]
  24. Fontana, A., et al. 2003, in preparation [Google Scholar]
  25. Giallongo, E., Menci, N., Poli, F., D'Odorico, S., & Fontana, A. 2000, ApJ, 530, 73 [Google Scholar]
  26. Giallongo, E., D'Odorico, S., Fontana, A., et al. 1998, AJ, 115, 2169 [NASA ADS] [CrossRef] [Google Scholar]
  27. Haykin, S. 1994, Neural Networks: A Comprehensive Foundation (NY: Macmillan) [Google Scholar]
  28. Kinney, A. L., Calzetti, D., Bohlin, R. C., et al. 1996, ApJ, 467, 38 [NASA ADS] [CrossRef] [Google Scholar]
  29. Labbe, I., Rudnick, G., Franx, M., & Daddi, E. 2003, ApJ, 591, 95 [Google Scholar]
  30. Le Borgne, D., & Rocca-Volmerange, B. 2002, A&A, 386, 446 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. Massarotti, M., Iovino, A., Buzzoni, A., & Valls-Gabaud, D. 2001, A&A, 380, 425 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  32. Moody, J. E. 1992, in The Effective number of parameters: An analysis of generalization and regularization in nonlinear learning systems, ed. J. E. Moody, S. J. Hanson, & R. P. Lippmann, Advances in Neural Information Processing Systems 4, 847, 854 [Google Scholar]
  33. Rigopoulou, D., Franceschini, A., Aussel, H., et al. 2000, ApJ, 537, L85 [NASA ADS] [CrossRef] [Google Scholar]
  34. Sarle, W. S. 1994, Neural Networks and Statistical Models, Proc. of the Nineteenth Annual SAS Users Group International Conf., Cary, NC: SAS Institute, 1538 ftp://ftp.sas.com/pub/neural/neural1.ps [Google Scholar]
  35. Sarle, W. S. 1994, in Neural Network Implementation in SAS Software, SAS Institute Inc., Proc. of the Nineteenth Annual SAS Users Group International Conf., Cary, NC: SAS Institute Inc., p 1551 ftp://ftp.sas.com/pub/neural/neural2.ps [Google Scholar]
  36. Sarle, W. S. 1995, Stopped training and other remedies for over-fitting, Proc. of the 27th Symp. on the interface of computing science and statistics, 352 [Google Scholar]
  37. Sawicki, M. J., Lin, H., & Yee, H. K. C. 1997, AJ, 113, 1S [NASA ADS] [CrossRef] [Google Scholar]
  38. Sawicki, M. J. M., & Ornelas, G. 2003, AJ, 126, 1208 [NASA ADS] [CrossRef] [Google Scholar]
  39. Sersic, J. L. 1968, Atlas de galaxias australes, Observatorio Astronomico, Cordoba [Google Scholar]
  40. Stoughton, C., Lupton, R. H., Bernardi, M., et al. 2002, AJ, 123, 485 [NASA ADS] [CrossRef] [Google Scholar]
  41. Tagliaferri, R., Longo, G., Andreon, S., et al. 2002 [arXiv:astro-ph/0203445] [Google Scholar]
  42. Trujillo, I., Rudnick, G., Rix, H. W., et al. 2004, ApJ, 604, 521 [NASA ADS] [CrossRef] [Google Scholar]
  43. Vanzella, E., Cristiani, S., Saracco, P., et al. 2001, AJ, 122, 2190 [NASA ADS] [CrossRef] [Google Scholar]
  44. Vanzella, E., Cristiani, S., Arnouts, S., et al. 2002, A&A, 396, 847 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  45. Wang, Y., Bahcall, N., & Turner, E. L. 1998, AJ, 116, 2081 [NASA ADS] [CrossRef] [Google Scholar]
  46. York, D. G., Adelman, J., Anderson, J. E., et al. 2000, AJ, 120, 1579 [Google Scholar]

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