A Bayesian classifier for symbol recognition

Conference object English OPEN
Barrat , Sabine; Tabbone , Salvatore; Nourrissier , Patrick;
  • Publisher: HAL CCSD
  • Subject: probabilistic graphical models | ACM : I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.3: Clustering | data analysis | variable selection | [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] | Symbol recognition | Bayesian networks
    acm: ComputingMethodologies_PATTERNRECOGNITION

URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf; International audience; We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly... View more
  • References (19)
    19 references, page 1 of 2

    1. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)

    2. Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58 (1996) 267-288

    3. Domingos, P., Pazzani, M.J.: Beyond independence: Conditions for the optimality of the simple bayesian classifier. In: International Conference on Machine Learning. (1996) 105-112

    4. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29(2-3) (1997) 131-163

    5. Friedman, J.H.: On bias, variance, 0/1loss, and the curse-of-dimensionality. Data Mining and Knowledge Discovery 1(1) (1997) 55-77

    6. Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1988)

    7. Robert, C.: A decision-Theoretic Motivation. Springer-Verlag (1997)

    8. Cooper, G.: The computational complexity of probabilistic inference using bayesian belief networks. Artificial Intelligent 42(2-3) (1990) 393-405

    9. Jaakkola, T., Jordan, M.I.: Variational probabilistic inference and the QMR-DT network. Journal of Artificial Intelligence Research 10 (1999) 291-322

    10. Jordan, M.I., Ghahramani, Z., Jaakkola, T., Saul, L.K.: An introduction to variational methods for graphical models. Machine Learning 37(2) (1999) 183-233

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