The NeuroBayes neural network package

Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

Volumn 559, Issue 1 SPEC. ISS., 2006, Pages 190-194

  Feindt, M ^a,b   Kerzel, U ^b  

^a Phi T Physics Information Technologies GmbH   (Germany)

^b UNIVERSITY OF KARLSRUHE   (Germany)

Author keywords

Bayes; Classification; Data mining; Density reconstruction; Neural network; Preprocessing

Indexed keywords

DATA MINING; HIGH ENERGY PHYSICS; PROBABILITY DENSITY FUNCTION; RISK MANAGEMENT; STATISTICAL METHODS;

BAYES; CLASSIFICATION; DENSITY RECONSTRUCTION; PREPROCESSING;

NEURAL NETWORKS;

EID: 33644948122     PISSN: 01689002     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.nima.2005.11.166     Document Type: Article

References (8)

1. 0 oscillation frequency at CDF, Ph.D. Thesis, Institut für Exp. Kernphysik, University of Karlsruhe, 2005.
2. C. Marino, THESIS TITLE, Diploma thesis, Institut für Exp. Kernphysik, University of Karlsruhe, 2005, in preparation.
3. M. Feindt, et al., BSAURUS - A Package For Inclusive B-Reconstruction, hep-ex/0102001.
4. Z. Albrecht, Analyse von orbital angeregten B-Mesonen, Ph.D. Thesis, Institut für Exp. Kernphysik, University of Karlsruhe, 2003.
5. M. Feindt, Efficient Neural Networking, Fifth Workshop of the EU-Research Training Network, The 3rd Generation as a Probe for New Physics, Karlsruhe, February 2005.
6. M. Feindt, Moderne statistische Methoden als Basis für individualisierte Risikovorhersage, Euroforum Konferenz Aktives Schadenmanagement, März 2003.
7. Data Mining Cup 2005, 〈www.data-mining-cup.de〉
8. M. Feindt, A Neural Bayesian Estimator for Conditional Probability Densities, arXiv:physics/0402093.