\begin{thebibliography}{99}

\addcontentsline{toc}{section}{Bibliography}

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\bibitem{FriedmanBook} 	J.~Friedman, T.~Hastie and R.~Tibshirani, 
								``{\em The  Elements of Statistical Learning}'', 
                        Springer Series in Statistics, 2001.

\bibitem{WebbBook}      A.~Webb, ``{\em Statistical Pattern Recognition}'',
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\bibitem{KunchevaBook}  L.I.~Kuncheva, ``{\em Combining Pattern Classifiers}'',
                        J.~Wiley \& Sons, 2004.

\bibitem{narsky}        I.~Narsky, 
                        ``{\em StatPatternRecognition: A C++ Package for 
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                        physics/0507143 (2005).

\bibitem{MVAreferences} The following web pages give information on available statistical
                        tools in HEP and other areas of science:
                        \urlsm{https://plone4.fnal.gov:4430/P0/phystat/},
                        \urlsm{http://astrostatistics.psu.edu/statcodes/}.

\bibitem{Cornelius}     The B{\small A}B{\small AR} Physics Book, 
                        B{\small A}B{\small AR} Collaboration 
                        (P.F.~Harrison and H.~Quinn (editors) \ea),
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\bibitem{Rarity}        To our information, the {\em Rarity} has been originally defined by 
                        F. Le Diberder in an unpublished Mark~II internal note. In a single 
                        dimension, as defined in Eq.~(\ref{eq:rarity}), it is equivalent to 
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\bibitem{kendall:stuart:ord:arnold:1999:2A}
                        M.~Kendall, A.~Stuart, K.J.~Ord, and S.~Arnold,
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\bibitem{citeulike:165404}
                        T.M.~Cover and J.A.~Thomas,
                        ``{\em Elements of information theory}'',
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\bibitem{PhysRevE.52.2318}
                        Y.-I. Moon, B.~Rajagopalan, and U.~Lall,
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\bibitem{kd-tree}       J.L.~Bentley, 
                        ``{\em Multidimensional Binary Search Trees Used for Associate Searching}'', 
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\bibitem{BinaryTree}    R.~Sedgewick, 
								``{\em Algorithms in C++}'', 
								Addison Wesley, Chapter 26, Boston, USA (1992).

\bibitem{CarliKoblitz}	T.~Carli and B.~Koblitz,
								Nucl. Instrum. Meth. A 501, 576 (2003) [hep-ex/0211019].

\bibitem{tfoam}         S.~Jadach, Comput. Phys. Commun. 130, 244 (2000); 
                        S.~Jadach, Comput. Phys. Commun. 152, 55 (2003).

\bibitem{pdefoam}       D.~Dannheim et al., Nucl. Instr. and Meth. A 606, 717 (2009).

\bibitem{Minuit}        F.~James, ``{\em MINUIT, Function Minization and ERROR Analysis}'', 
                        Version 94.1, CERN program Library long writeup D506.

\bibitem{VanLaarhovenEA}P.J.M.~Van Laarhoven and E.H.L.~Aart,  
                        ``{\em Simulated Annealing: Theory and Application}'',
                        D.~Reidel Publishing, Dordrecht, Holland, 1987.

\bibitem{MetropolisEA}  N.~Metropolis, A.W.~Rosenbluth, M.N.~Rosenbluth, A.M.~Teller, 
                        and E.~Teller,
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\bibitem{353qh}         353QH twice smoothing algorithm, 
                        presented by J. Friedman in Proc. of the 1974 CERN School of 
                        Computing, Norway, Aug 11-24, 1974.

\bibitem{scott}         D.W.~Scott, ``{\em Multivariate Density Estimation, Theory, Practice, 
                        and Visualization}'', Wiley-Interscience, New York, 1992.

\bibitem{Fisher}        R.A.~Fisher, Annals Eugenics 7, 179 (1936).

\bibitem{Mahalanobis}   P.C.~Mahalanobis, Proc. Nat. Inst. Sci. India, Part 2A, 49 (1936);
                        P.C.~Mahalanobis, 
                        ``{\em On the generalized distance in statistics}'', 
                        Proceedings of the National Institute of Science, Calcutta, 
                        12, 49 (1936).

\bibitem{BFGS}         C.G.~Broyden, ``{\em The Convergence of a Class of Double-rank Minimization Algorithms}'', J. Inst. of Math. and App. 6, 76 (1970); R.~Fletcher, ``{\em A New Approach to Variable Metric Algorithms}'', Computer J. 13, 317 (1970);
D.~Goldfarb, ''{\em A Family of Variable Metric Updates Derived by Variational Means}'', Math. Comp. 24, 23 (1970); D.F.~Shannon, ``{\em Conditioning of Quasi-Newton Methods for Function Minimization}'', Math. Comp. 24, 647 (1970).

\bibitem{Boosting}      R.E.~Schapire, ``{\em The strenght of weak learnability}, (1990) Machine Learning 5 197-227;
                        Y.~Freud, ``{\em Boosting a weak learning algorithm by majority} (1995) Inform. and Comput. {\bf 121} 256-285;

\bibitem{AdaBoost}      Y.~Freund and R.E.~Schapire, J. of Computer and System Science 
                        55, 119 (1997).

\bibitem{RealAdaBoost}  Robert E. Schapire, Yoram Singer, {\em Improved Boosting Algorithms Using Confidence-rated Predictions},  Machine Learning , Vol.37, No. 3, (297-336) 1999.
 
\bibitem{AdaBoostR2} H.~Drucker,{\em Improving regressors using
  boosting techniques},In D. H. Fisher (Ed.), Proceedings of the
  fourteenth international conference on machine learning (ICML 1997)
  (pp. 107�115). Nashville, TN, USA, July 8�12. Morgan Kaufmann, ISBN
  1�55860�486�3.

\bibitem{AdaCost} Wei Fan and Salvatore J. Stolfo, {\em AdaCost: misclassification cost-sensitive boosting}, Proceedings of the 16th International conference on machine learning (ICML 1999).

\bibitem{Breiman2001}   L.~Breiman, {\em Random Forests}, Technical Report, University of California 2001.


\bibitem{Quinlan}       Y.R.~Quinlan, ``{\em Simplifying Decision Trees}'',
                        Int. J. Man-Machine Studies, 28, 221 (1987).

\bibitem{Breiman1984}   L.~Breiman, J.~Friedman, R.~Olshen and C.~Stone, 
                        ``{\em Classification and Regression Trees}'',
                        Wadsworth (1984).

\bibitem{parallelcoor}  We use the ROOT class \code{TParallelCoord} by B. Dalla Piazza, 2007.

\bibitem{RuleFit} 	   J.~Friedman and B.E.~Popescu, 
								``{\em Predictive Learning via Rule Ensembles}'', 
                        Technical Report, Statistics Department, Stanford University, 2004.

\bibitem{RuleFitMin}    J.~Friedman and B.E.~Popescu, 
								``{\em Gradient Directed Regularization for Linear 
                          Regression and Classification}'', 
                        Technical Report, Statistics Department, Stanford University, 2003.

\bibitem{RuleFitWeb}    RuleFit web site:
                        \urlsm{http:/www-stat.stanford.edu/~jhf/r-rulefit/RuleFit_help.html}.

\bibitem{RuleSusy}	   J.~Conrad and F.~Tegenfeldt,
                        JHEP 0607, 040 (2006) [hep-ph/0605106].

\bibitem{SLIPPER}	      W.~Cohen and Y.~Singer,
			               Proceedings of the Sixteenth National Conference on Artificial 
                        Intelligence (AAAI-99), 335, AAAI Press, 1999.

%%% SVM
\bibitem{Vapnik1963}   V.~Vapnik and A.~Lerner, 
                       ``{\em Pattern recognition using generalized portrait method}'',
                       Automation and Remote Control, 24, 774 (1963). 

\bibitem{Vapnik1964}   V.~Vapnik and A.~Chervonenkis,
                       ``{\em A note on one class of perceptrons}'',
                       Automation and Remote Control, 25 (1964).

\bibitem{Vapnik1992}   B.E.~Boser, I.M.~Guyon, and V.N.~Vapnik, 
                       ``{\em A training algorithm for optimal margin classifiers}'', 
                       in D.~Haussler, ed., Proceedings of the 5th Annual ACM Workshop 
                       on Computational Leraning Theory, 144, ACM Press (1992). 

\bibitem{Vapnik1995a}  C.~Cortes and V.~Vapnik, 
                       ``{\em Support vector networks}'', 
                       Machine Learning, 20, 273 (1995). 

\bibitem{Vapnik1995b}  V.~Vapnik, ``{\em The Nature of Statistical Learning Theory}'', 
                       Springer Verlag, New York, 1995.

\bibitem{Burges}       C.J.C.~Burges, 
                       ``{\em A Tutorial on Support Vector Machines for Pattern Recognition}'', 
                       Data Mining and Knowledge Discovery, 2, 1 (1998).

\bibitem{Platt}        J.~Platt,
                       ``{\em Fast training of support vector machines using sequential 
                       minimal optimization}'', 
                       in B.~Scholkopf, C.~Burges and A.~Smola, eds., 
                       Advances in Kernel Methods -- Support Vector Learning, 
                       ch. 12, pp. 185, MIT Press, 1999.

\bibitem{Keerthi}      S.~Keerthi, S.~Shevade, C.~Bhattacharyya and K.~Murthy, 
                       ``{\em Improvements to Platt's SMO algorithm for SVM classifier 
                       design}'', 
                       Technical Report CD-99-14, Dept. of Mechanical and Production Engineering, 
                       Natl. Univ. Singapore, Singapore, 1999.

\bibitem{Huber}	      P.~Huber,
                        ``{\em Robust estimation of a location parameter}'',
                        Annals of Mathematical Statistics, 35, 73-101,1964

%Deep Learning
\bibitem{higgs_dnn}     P.~Baldi, P.~Sadowski, D.~Whiteson,
                        ``{\em Searching for Exotic Particles in High-Energy Physics 
                         with Deep Learning}'', Nature Communications 5,
                       Article number: 4308 (2014)

\bibitem{dl_optimizers}  S.~Ruder, ``{\em An overview of gradient descent optimization algorithms}'',
  see   \urlsm{https://ruder.io/optimizing-gradient-descent/}.

 \bibitem{alexnet}
   A. Krizhevsky, I. Sutskever, G. E. Hinton (2017). ``{\em ImageNet classification with deep convolutional neural networks}'',
   see \urlsm{https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks}

 \bibitem{batch-norm}
   S. Ioffe,  C. Szegedy (2015). ``{\em Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift}". arXiv:1502.03167


 \bibitem{deeplearning_book}
   @book{Goodfellow-et-al-2016,
    title={Deep Learning},
    author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
    publisher={MIT Press},
    note={\url{http://www.deeplearningbook.org}},
    year={2016}
  }

\bibitem{lstm}
  S. Hochreiter; J. Schmidhuber (1997). ``{\em Long short-term memory}". Neural Computation. 9 (8): 1735–1780.

\bibitem{gru}
K. Cho et al. (2014). ``{\em Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation"}. arXiv:1406.1078


  
% Cross Validation
\bibitem{Hastie01a}     T.~Haste et al.,
                        ``The Elements of Statistical Learning'',
                        Springer, (2001).

\bibitem{Geisser75}     Seymour Geisser,
                        ``The Predictive Sample Reuse Method with Applications'',
                        Journal of the American Statistical Association,
                        vol. 70:350, p. 320-328, (1975).


\end{thebibliography}
