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Novel approaches of data-mining in experimental physics

[1] HAN, J.-KAMBER, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco, 2000.Search in Google Scholar

[2] LEBEDEV, S.-OSOSKOV, G.: Fast algorithms for ring recognition and electron iden- tification in the CBM RICH detector, Phys. Particles Nuclei Lett. 6 (2009), 161-176.10.1134/S1547477109020095Search in Google Scholar

[3] LEBEDEV, S.-HOEHNE, C.-OSOSKOV, G.: Status of the electron identification algo- rithms for the RICH and TRD detectors in the CBM experiment, CBM Progress Report 2010, GSI, Darmstadt, 2010, p. 73.Search in Google Scholar

[4] DMITRIEVSKY, S.: On behalf of the OPERA collaboration, status of the OPERA neu- trino oscillation experiment, Acta Phys. Polon. B 41 (2010), 1539-1546.Search in Google Scholar

[5] OSOSKOV, G. A.-POLANSKI, A.-PUZYNIN, I. V.: Current methods of processing experimental data in high energy physics, Phys. Particles Nuclei 33 (2002), 347-382.Search in Google Scholar

[6] HAYKIN, S.: Neural Networks: A Comprehensive Foundation (2nd ed.). IEEE, New York, NY, 1999.Search in Google Scholar

[7] http://cbmroot.gsi.de. Search in Google Scholar

[8] TMVA Users Guide http://tmva.sf.net Search in Google Scholar

[9] FREUND, Y.-SCHAPIRE, R. E.: A short introduction to boosting, J. Japanese Soc. Artificial Intelligence 14 (1999), 771-780.Search in Google Scholar

[10] DURAN, B. S.-ODELL, P. L.: Cluster Analysis: A Survey, in: Lecture Notes in Econom. and Math. Systems. Econometrics, Vol. 100, Springer-Verlag, New York, 1974.Search in Google Scholar

[11] MITSYN, S. V.-OSOSKOV, G. A.: The growing neural gas and clustering of large amounts of data, Optical Memory & Neural Networks 20 (2011), 260-270.10.3103/S1060992X11040060Search in Google Scholar

ISSN:
1210-3195
Idioma:
Inglés
Calendario de la edición:
3 veces al año
Temas de la revista:
Mathematics, General Mathematics