Open Access
EPJ Web of Conferences
Volume 55, 2013
SOS 2012 – IN2P3 School of Statistics
Article Number 02004
Number of page(s) 25
Section Multivariate Analysis Tools
Published online 01 July 2013
  1. L. Breiman, J.H. Friedman, R.A. Olshen and C.J. Stone, Classification and Regression Trees, Wadsworth, Stamford, 1984
  2. I. Narsky, “StatPatternRecognition: A C++ Package for Statistical Analysis of High Energy Physics Data”, arXiv:physics/0507143, 2005.
  3. C. Gini, “Variabilità e Mutabilità” (1912), reprinted in Memorie di Metodologica Statistica, edited by E. Pizetti and T. Salvemini, Rome: Libreria Eredi Virgilio Veschi, 1955.
  4. J.R. Quinlan, “Simplifying decision trees”, International Journal of Man-Machine Studies, 27(3):221–234, 1987. [CrossRef]
  5. H. Prosper, Multivariate discriminants, “Ensemble learning”, EPJ Web of Conferences 4 02001, 2010, SOS’08 - School of Statistics.
  6. R.E. Schapire, “The strength of weak learnability”, Machine Learning, 5(2):197–227, 1990.
  7. Y. Freund, “Boosting a weak learning algorithm by majority”, Information and Computation. 121(2):256–285, 1995. [CrossRef]
  8. Y. Freund and R.E. Schapire, “Experiments with a New Boosting Algorithm” in Machine Learning: Proceedings of the Thirteenth International Conference, edited by L. Saitta (Morgan Kaufmann, San Francisco) p. 148, 1996.
  9. B.P. Roe, H.-J. Yang, J. Zhu, Y. Liu, I. Stancu, and G. McGregor, Nucl. Instrum. Methods Phys. Res., Sect.A 543, 577, 2005; [CrossRef]
  10. H.-J. Yang, B.P. Roe, and J. Zhu, Nucl. Instrum. Methods Phys. Res., Sect. A 555, 370, 2005. [NASA ADS] [CrossRef]
  11. V. M. Abazov et al. [D0 Collaboration], “Evidence for production of single top quarks and first direct measurement of |Vtb|”, Phys. Rev. Lett. 98, 181802, 2007; [CrossRef] [PubMed]
  12. V. M. Abazov et al., “Evidence for production of single top quarks”, Phys. Rev. D78, 012005, 2008;
  13. V. M. Abazov et al., “Observation of single top quark production”, Phys. Rev. Lett. 103, 092001, 2009. [CrossRef] [PubMed]
  14. Y. Freund and R.E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting”, Journal of Computer and System Sciences, 55(1):119–139, 1997. [CrossRef]
  15. J.H. Friedman, T. Hastie and R. Tibshirani, “Additive logistic regression: a statistical view of boosting”, The Annals of Statistics, 28(2), 377–386, 2000. [CrossRef] [MathSciNet]
  16. H. Prosper, Multivariate discriminants, “Optimal classification”, EPJ Web of Conferences 4 02001, 2010, SOS’08 - School of Statistics.
  17. A. Höcker et al., “TMVA: Toolkit for multivariate data analysis”, PoS ACAT, 040, CERNOPEN-2007-007, arXiv:physics/0703039, 2007.
  18. G. Cowan, “Multivariate statistical methods and data mining in particle physics”, CERN Academic Training Lectures, June 2008.
  19. J.H. Friedman, “Greedy function approximation: a gradient boosting machine”, The Annals of Statistics, 29 (5), 1189–1232, 2001. [CrossRef]
  20. Y. Freund, “An adaptive version of the boost by majority algorithm”, Machine Learning, 43 (3), 293–318, 2001. [CrossRef]
  21. L. Breiman, “Bagging Predictors”, Machine Learning, 24 (2), 123–140, 1996.
  22. L. Breiman, “Random forests”, Machine Learning, 45 (1), 5–32, 2001. [CrossRef]
  23. J.R. Quinlan, “Induction of decision trees”, Machine Learning, 1(1):81–106, 1986.
  24. J.R. Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993.

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