EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 605 (September 2017) A&A, 605 (2017) A123 References
Free Access
Issue
A&A
Volume 605, September 2017
Article Number A123
Number of page(s) 14
Section Catalogs and data
DOI https://doi.org/10.1051/0004-6361/201628937
Published online 20 September 2017
  1. Bass, G., & Borne, K. 2016, MNRAS, 459, 3721 [NASA ADS] [CrossRef] [Google Scholar]
  2. Bessel, M. S., & Brett, J. M. 1988, PASP, 100, 1134 [NASA ADS] [CrossRef] [Google Scholar]
  3. Beygelzimer, A., Kakadet, S., Langford, J., et al. 2013, FNN: Fast Nearest Neighbor Search Algorithms and Applications, r package version 1.1 [Google Scholar]
  4. Biau, G., Devroye, L., & Lugosi, G. 2008, J. Mach. Learn. Res., 9, 2015 [Google Scholar]
  5. Breiman, L. 1996, Mach. Learn., 24, 123 [Google Scholar]
  6. Breiman, L. 2001, Mach. Learn., 45, 5 [CrossRef] [Google Scholar]
  7. Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. 1984, Classification and Regression Trees, 1st edn. (New York, N.Y.: Chapman and Hall/CRC) [Google Scholar]
  8. Brys, G., Hubert, M., & Struyf, A. 2004, J. Comput. Graph. Stat., 13 [Google Scholar]
  9. Brys, G., Hubert, M., & Struyf, A. 2006, Computational statistics & data analysis, 50, 733 [CrossRef] [Google Scholar]
  10. Chang, C.-C., & Lin, C.-J. 2011, ACM Transactions on Intelligent Systems and Technology (TIST), 2, 27 [Google Scholar]
  11. Chen, T., He, T., & Benesty, M. 2015, xgboost: Extreme Gradient Boosting, r package version 0.4-2 [Google Scholar]
  12. Collins, G. W. 1987, Physics of Be stars, eds. A. Slettebak, & T. P. Snow, Proc. IAU Coll., 92 (Cambridge University Press) [Google Scholar]
  13. Cortes, C., & Vapnik, V. 1995, Mach. Learn., 20, 273 [Google Scholar]
  14. Cristianini, N., & Shawe-Taylor, J. 2000, An introduction to support vector machines and other kernel-based learning methods (Cambridge University press) [Google Scholar]
  15. Deb, S., & Singh, H. P. 2009, A&A, 507, 1729 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  16. Debosscher, J., Sarro, L. M., Aerts, C., et al. 2007, A&A, 475, 1159 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  17. Fix, E., & Hodges Jr, J. L. 1951, Discriminatory analysis-nonparametric discrimination: consistency properties, Tech. Rep., DTIC Document [Google Scholar]
  18. Friedman, J. H. 2001, The Annals of Statistics, 29, 1189 [Google Scholar]
  19. Graczyk, D., Soszyński, I., Poleski, R., et al. 2011, Acta Astron., 61, 103 [Google Scholar]
  20. Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. 1986, Robust statistics: the approach based on influence functions (John Wiley & Sons) [Google Scholar]
  21. Hastie, T., Tibshirani, R., & Friedman, J. 2009, The Elements of Statistical Learning, Springer Series in Statistics (New York, NY: Springer New York) [Google Scholar]
  22. Hatano, H., Kadowaki, R., Nakajima, Y., et al. 2006, AJ, 132, 2653 [NASA ADS] [CrossRef] [Google Scholar]
  23. Hernández, J., Calvet, N., Hartmann, L., et al. 2005, AJ, 129, 856 [NASA ADS] [CrossRef] [Google Scholar]
  24. Hsu, C.-W., & Lin, C.-J. 2002, Neural Networks, IEEE Transactions on, 13, 415 [Google Scholar]
  25. Huber, P. J. 1964, The Annals of Mathematical Statistics, 35, 73 [Google Scholar]
  26. Hubert, A. M., & Floquet, M. 1998, A&A, 335, 565 [NASA ADS] [Google Scholar]
  27. Huber, P. J., & Ronchetti, E. M. 2009, Robust Statistics, 2nd edn. (Hoboken, N.J: Wiley) [Google Scholar]
  28. Hubert, A. M., Floquet, M., & Zorec, J. 2000, The Be Phenomenon in Early-Type Stars., eds. M. A. Smith, H. F. Henrichs, & J. Fabregat (Astron. Soc. Pac.), Proc. IAU Coll., 92, 175 [Google Scholar]
  29. Khun, M. 2016, caret: Classification and Regression Training, r package version 6.0-64 [Google Scholar]
  30. Kim, D.-W., Protopapas, P., Bailer-Jones, C. A. L., et al. 2014, A&A, 566, A43 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. Koenig, X. P., & Leisawitz, D. T. 2014, ApJ, 791, 131 [NASA ADS] [CrossRef] [Google Scholar]
  32. Krijthe, J. 2015, Rtsne: T-Distributed Stochastic Neighbor Embedding using Barnes-Hut Implementation, r package version 0.10 [Google Scholar]
  33. Liaw, A., & Wiener, M. 2002, R News, 2, 18 [Google Scholar]
  34. Mennickent, R. E., Pietrzyński, G., Gieren, W., & Szewczyk, O. 2002, A&A, 393, 887 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  35. Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., & Leisch, F. 2015, e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien, r package version 1.6-7 [Google Scholar]
  36. Mowlavi, N. 2014, A&A, 568, A78 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  37. Park, M., Oh, H.-S., & Kim, D. 2013, PASP, 125, 470 [NASA ADS] [CrossRef] [Google Scholar]
  38. Pawlak, M., Graczyk, D., Soszyński, I., et al. 2013, Acta Astron., 63, 323 [NASA ADS] [Google Scholar]
  39. Pichara, K., Protopapas, P., & León, D. 2016, ApJ, 819, 18 [NASA ADS] [CrossRef] [Google Scholar]
  40. Poleski, R., Soszyński, I., Udalski, A., et al. 2010, Acta Astron., 60, 1 [EDP Sciences] [Google Scholar]
  41. R Core Team 2015, R: A Language and Environment for Statistical Computing, (Vienna, Austria: R Foundation for Statistical Computing) [Google Scholar]
  42. Rivinius, T., Carciofi, A. C., & Martayan, C. 2013, A&ARv, 21, 69 [Google Scholar]
  43. Sabogal, B. E., Mennickent, R. E., Pietrzyński, G., & Gieren, W. 2005, MNRAS, 361, 1055 [NASA ADS] [CrossRef] [Google Scholar]
  44. Sabogal, B. E., Mennickent, R. E., Pietrzyński, G., et al. 2008, A&A, 478, 659 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  45. Sabogal, B. E., García-Varela, A., & Mennickent, R. E. 2014, PASP, 126, 219 [NASA ADS] [CrossRef] [Google Scholar]
  46. Samworth, R. J. 2012, The Annals of Statistics, 40, 2733 [CrossRef] [Google Scholar]
  47. Sarro, L. M., Debosscher, J., López, M., & Aerts, C. 2009, A&A, 494, 739 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  48. Schlafly, E., & Finkbeiner, D. 2011, ApJ, 737, 103 [NASA ADS] [CrossRef] [Google Scholar]
  49. Silverman, B. W., & Jones, M. C. 1989, International Statistical Review/Revue Internationale de Statistique, 57, 233 [Google Scholar]
  50. Soszyński, I., Poleski, R., Udalski, A., et al. 2008a, Acta Astron., 58, 163 [NASA ADS] [Google Scholar]
  51. Soszyński, I., Udalski, A., Szymański, M. K., et al. 2008b, Acta Astron., 58, 293 [NASA ADS] [Google Scholar]
  52. Soszyński, I., Udalski, A., Szymański, M. K., et al. 2009a, Acta Astron., 59, 1 [Google Scholar]
  53. Soszyński, I., Udalski, A., Szymański, M. K., et al. 2009b, Acta Astron., 59, 239 [Google Scholar]
  54. Soszyński, I., Poleski, R., Udalski, A., et al. 2010a, Acta Astron., 60, 17 [NASA ADS] [Google Scholar]
  55. Soszyński, I., Udalski, A., Szymański, M. K., et al. 2010b, Acta Astron., 60, 91 [NASA ADS] [Google Scholar]
  56. Soszyński, I., Dziembowski, W. A., Udalski, A., et al. 2011a, Acta Astron., 61, 1 [NASA ADS] [Google Scholar]
  57. Soszyński, I., Udalski, A., Pietrukowicz, P., et al. 2011b, Acta Astron., 61, 285 [NASA ADS] [Google Scholar]
  58. Soszyński, I., Udalski, A., Szymański, M. K., et al. 2011c, Acta Astron., 61, 217 [NASA ADS] [Google Scholar]
  59. Soszyński, I., Udalski, A., Poleski, R., et al. 2012, Acta Astron., 62, 219 [NASA ADS] [Google Scholar]
  60. Soszyński, I., Udalski, A., Pietrukowicz, P., et al. 2013a, Acta Astron., 63, 37 [NASA ADS] [Google Scholar]
  61. Soszyński, I., Udalski, A., Szymański, M. K., et al. 2013b, Acta Astron., 63, 21 [NASA ADS] [Google Scholar]
  62. Staudte, R. G., & Sheather, S. J. 1990, Robust estimation and testing, Wiley Series in Probability and Statistics (John Wiley & Sons) [Google Scholar]
  63. Therneau, T., Atkinson, B., & Ripley, B. 2015, rpart: Recursive Partitioning and Regression Trees, r package version 4.1–10 [Google Scholar]
  64. Udalski, A. 2004, Acta Astron., 53, 291 [Google Scholar]
  65. Udalski, A., Szymański, M. K., & Szymański, G. 2015, Acta Astron., 65, 138 [Google Scholar]
  66. Van der Maaten, L., & Hinton, G. 2008, J. Mach. Learn. Res., 9, 85 [Google Scholar]
  67. Venables, W. N., & Ripley, B. D. 2013, Modern applied statistics with S-PLUS (Springer Science & Business Media) [Google Scholar]
  68. Von Neumann, J. 1941, The Annals of Mathematical Statistics, 12, 367 [CrossRef] [Google Scholar]
  69. Wisniewski, J. P., & Bjorkman, K. S. 2006, ApJ, 652, 458 [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.