Model selection in top quark tagging with a support vector classifier

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn 3, Issue , 2004, Pages 2059-2064

  Anguita, Davide ^a   Ridella, Sandro ^a   Rivieccio, Fabio ^a   Zunino, Rodolfo ^a   Amerio, Silvia ^b   Lazzizzera, Ignazio ^b  

^a UNIVERSITY OF GENOA   (Italy)

^b UNIVERSITY OF TRENTO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BOOTSTRAP TECHNIQUES; COLLIDER DETECTOR AT FERMILAB (CDF); SUPPORT VECTOR CLASSIFIERS (SVC); SUPPORT VECTOR MACHINES (SVM);

CLASSIFICATION (OF INFORMATION); COLLIDING BEAM ACCELERATORS; COMPUTER SIMULATION; DATA REDUCTION; ERROR ANALYSIS; LEARNING SYSTEMS; MATRIX ALGEBRA; MONTE CARLO METHODS; QUADRATIC PROGRAMMING;

PARTICLE DETECTORS;

EID: 10844249293     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2004.1380934     Document Type: Conference Paper

References (19)

1. D.Anguita, A.Boni, S.Ridella, "Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap", Neural Processing Letters, Vol. 11, N.1, pp.51-58, 2000.
2. D.Anguita, S.Ridella, F.Rivieccio and R.Zunino, "Hyperparameter design criteria for support vector classifiers", Neurocomputing, Vol. 55, Issues 1-2, pp. 109-134, September 2003
3. C.Cortes, V.Vapnik, "Support Vector Networks", Machine Learning, Vol. 20, pp.273-297, 1995.
4. C.Cortes, M.Mohri, "AUC Optimization vs. Error Rate Minimization", In Advances in Neural Information Processing Systems (NIPS 2003), Vol. 16, Vancouver, Canada, MIT Press, 2004.
5. T.M.Cover, P.E.Hart, "Nearest Neighbor Pattern Classification", IEEE Transactions on Information Theory, Vol. IT-13(1), pp.21-27, 1967.
6. B.Efron, R.J.Tibshirani, An Introduction to the Bootstrap. Monographs on Statistics and Applied Probability, Chapman & Hall, New York, 1993.
7. R.A.Fisher, The Use of Multiple Measurements in Taxonomic Problems, Annals of Eugenics, Vol. 7, part II, pp. 179-188, 1936.
8. Isabelle Guyon, Online SVM Application list. (See http://www.clopinet.com/isabelle/Projects/SVM/applist.html)
9. S. Abachi et al., "Direct Measurement of the Top Quark Mass", hep-ex/9703008, submitted to Physical Review Letters, 1997.
10. K.Duan, S.Keerthi, A.Poo, Evaluation of simple performance measures for tuning svm hyperparameters, Neurocomputing, Vol. 51, 2003, pp. 41-59.
11. E.Laenen, J. Smith, W.L.van Neerven, "Top-quark production cross section", Physics Letters, B 321, pp.254-258, 1994.
12. P.Bartlett, S.Boucheron, G.Lugosi, "Model selection and error estimation", Machine Learning, Vol. 48, Number 1-3 pp.85-113, 2002.
13. G.Parisi, "Superinclusive cross sections", Physics Letters, B 74, pp.65-67, 1978.
14. T.Sjostrand, "The Lund Monte Carlo for jet fragmentation and e/sup +/e/sup -/ physics-JETSET version 6.2", Computer Physics Communications, 39(3), pp.347-407, 1986.
15. J.P.Egan, Signal Detection Theory and ROC Analysis, Academic Press, 1975.
16. A.Sidoti, Ph. D. Thesis, Univ. Trento, 1999.
17. P.Vannerem, K.-R.Müller, A.J.Smola, B.Schölkopf, and S.Sldner-Rembold, "Classifying lep data with support vector algorithms", In Proceedings of AIHENP'99, 1999.
18. F. Abe, et al., "First observation of the all-hadronic decay of tt pairs", Physical Review Letters, 79, 1992, (1997).
19. V.Vapnik, Statistical Learning Theory, Wiley, Chichester, 1998.