Free Access
Issue
A&A
Volume 622, February 2019
Article Number A124
Number of page(s) 10
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/201834237
Published online 06 February 2019
  1. Alpaydin, E. 2010, Introduction to Machine Learning, 2nd edn. (Cambridge: The MIT Press) [Google Scholar]
  2. Antia, H. M., & Basu, S. 2007, Astron. Nachr., 328, 257 [NASA ADS] [CrossRef] [Google Scholar]
  3. Asensio Ramos, A., Requerey, I. S., & Vitas, N. 2017, A&A, 604, A11 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  4. Bishop, C. M. 2006, Pattern Recognition and Machine Learning (Information Science and Statistics) (Berlin, Heidelberg: Springer-Verlag) [Google Scholar]
  5. Bogart, R. S., Baldner, C., Basu, S., Haber, D. A., & Rabello-Soares, M. C. 2011a, J. Phys. Conf. Ser., 271, 012008 [NASA ADS] [CrossRef] [Google Scholar]
  6. Bogart, R. S., Baldner, C., Basu, S., Haber, D. A., & Rabello-Soares, M. C. 2011b, J. Phys. Conf. Ser., 271, 012009 [CrossRef] [Google Scholar]
  7. Breiman, L. 2001, Mach. Learn., 45, 5 [CrossRef] [Google Scholar]
  8. Friedman, J. H. 2000, Ann. Stat., 29, 1189 [CrossRef] [Google Scholar]
  9. Geurts, P., Ernst, D., & Wehenkel, L. 2006, Mach. Learn., 63, 3 [CrossRef] [Google Scholar]
  10. Giles, P. M., Duvall, T. L., Scherrer, P. H., & Bogart, R. S. 1997, Nature, 390, 52 [NASA ADS] [CrossRef] [Google Scholar]
  11. Gizon, L., & Birch, A. C. 2005, Liv. Rev. Sol. Phys., 2, 6 [Google Scholar]
  12. Glorot, X., Bordes, A., & Bengio, Y. 2011, in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, eds. G. Gordon, D. Dunson, & M. Dudík (Fort Lauderdale, FL, USA: PMLR), Proc. Mach. Learn. Res., 15, 315 [Google Scholar]
  13. Hand, D. J., Smyth, P., & Mannila, H. 2001, Principles of Data Mining (Cambridge: MIT Press) [Google Scholar]
  14. Härdle, W., & Simar, L. 2007, Applied Multivariate Statistical Analysis, 2nd edn. (Berlin, Heidelberg: Springer) [Google Scholar]
  15. Hardoon, D. R., Szedmak, S., & Shawe-Taylor, J. 2004, Neur. Comput., 16, 2639 [CrossRef] [Google Scholar]
  16. Hastie, T., Tibshirani, R., & Friedman, J. 2001, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics (New York: Springer, New York Inc.) [Google Scholar]
  17. Haykin, S. 1998, Neural Networks: A Comprehensive Foundation, 2nd edn. (Upper Saddle River, NJ, USA: Prentice Hall PTR) [Google Scholar]
  18. Haykin, S. S. 2009, Neural Networks and Learning Machines (London: Pearson Education) [Google Scholar]
  19. Hill, F. 1988, ApJ, 333, 996 [NASA ADS] [CrossRef] [Google Scholar]
  20. Hotelling, H. 1936, Biometrika, 28, 321 [CrossRef] [Google Scholar]
  21. Juszczak, P., Tax, D. M. J., & Duin, R. P. W. 2002, Proceedings of the 8th Annu. Conf. Adv. School Comput. Imaging, 25 [Google Scholar]
  22. Little, R., & Rubin, D. 1987, Statistical Analysis With Missing Data, Wiley Series in Probability and Statistics – Applied Probability and Statistics Section Series (New York: Wiley) [Google Scholar]
  23. Löptien, B., Gizon, L., Birch, A., et al. 2018, Nat. Astron., 2, 568 [NASA ADS] [CrossRef] [Google Scholar]
  24. Mosteller, F., & Tukey, J. W. 1968, in Handbook of Social Psychology, eds. G. Lindzey, & E. Aronson (Boston: Addison-Wesley), 2 [Google Scholar]
  25. Pearson, K. A., Palafox, L., & Griffith, C. A. 2018, MNRAS, 474, 478 [NASA ADS] [CrossRef] [Google Scholar]
  26. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  27. Raissi, M., Perdikaris, P., & Karniadakis, G. E. 2017a, ArXiv e-prints [arXiv:1711.10561] [Google Scholar]
  28. Raissi, M., Perdikaris, P., & Karniadakis, G. E. 2017b, ArXiv e-prints [arXiv:1711.10566] [Google Scholar]
  29. Rokach, L., & Maimon, O. 2014, Data Mining With Decision Trees: Theory and Applications, 2nd edn. (Singapore: World Scientific Publishing Co., Inc.) [CrossRef] [Google Scholar]
  30. Schou, J., & Bogart, R. S. 1998, ApJ, 504, L131 [NASA ADS] [CrossRef] [Google Scholar]
  31. Schou, J., Scherrer, P. H., Bush, R. I., et al. 2012, Sol. Phys., 275, 229 [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.