Novel mixtures based on the dirichlet distribution: Application to data and image classification

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2734, Issue , 2003, Pages 172-181

  Bouguila, Nizar ^a   Ziou, Djemel ^a   Vaillancourt, Jean ^a  

^a UNIVERSITÉ DE SHERBROOKE   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; DATABASE SYSTEMS; IMAGE ANALYSIS; MATRIX ALGEBRA; PATTERN RECOGNITION; PROBLEM SOLVING; VECTORS; ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); DATA MINING; LEARNING SYSTEMS; MIXTURES;

GENERALIZED DIRICHLET DISTRIBUTION (GDD); IMAGE CLASSIFICATION; MULTINOMIAL DIRICHLET DISTRIBUTIONS (MDD); DIRICHLET DISTRIBUTIONS; DISCRETE DATA; GAUSSIANS; GENERALIZED DIRICHLET DISTRIBUTION; HIGH FLEXIBILITY; IMAGE DATABASE; MODEL DATA; MULTINOMIALS;

CLASSIFICATION (OF INFORMATION); IMAGE CLASSIFICATION;

EID: 8344258373     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-45065-3_15     Document Type: Conference Paper

References (13)

1. Amari, S. Natural Gradient Works Efficiently in Learning. Neural Computation, 10:251-276, 1998.
2. Bouguila, N., Ziou, D. and Vaillancourt, J. The Introduction of Dirichlet Mixture into Image Processing Applications. Submitted to. IEEE Transactions on Image Processing.
3. Crawford, S.L. An Application of the Laplace Method to Finite Mixture Distributions. Journal of the American Statistical Association, 89:259-267, 1994.
4. Dempster, A.P., Laird, N.M. and Rubin, D.B. Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, B, 39:1-38, 1977.
5. Duda, R.O. and Hart, P.E. Pattern Classification and Scene Analysis. Wiley, New York, 1973.
6. Everitt, B.S. and Hand, D.J. Finite mixture Distributions. Chapman and Hall, London, UK, 1981.
7. Fielitz, B.D and Myers, B.L. Estimation of Parameters in the Beta Distribution. Decision Sciences, 6:1-13, 1975.
8. Ikeda, S. Acceleration of the EM algorithm. Systems and Computers in Japan, 31(2):10-18, February 2000.
9. Kaufman, L. and Rousseeuw, P.J. Finding Groups in Data. John Wiley, New York, 1990.
10. Kherfi, M.L., Ziou, D. and Bernardi, A. Content-Based Image Retrieval Using Positive and Negative Examples. To appear, 2002.
11. Kotz, S. and Ng, K.W. and Fang, K. Symmetric Multivariate and Related Distributions. London/New York: Chapman and Hall, 1990.
12. Raftery, A.E. and Banfield, J.D. Model-Based Gaussian and Non-Gaussian Clustering. Biometrics, 49:803-821, 1993.
13. Rao, C.R. Advanced Statistical Methods in Biomedical Research. New York: John Wiley and Sons, 1952.