A regression model with a hidden logistic process for signal parametrization

ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning

Volumn , Issue , 2009, Pages 503-508

  Chamroukhi, F ^a,b   Same A ^a   Govaert, G ^b   Aknin, P ^a  

^a French National Institute for Transport and Safety Research   (France)

^b UNIVERSITÉ DE TECHNOLOGIE DE COMPIÈGNE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

EXPECTATION-MAXIMIZATION ALGORITHMS; ITERATIVE REWEIGHTED LEAST SQUARE; LOGISTIC PROCESS; MAXIMUM LIKELIHOOD METHODS; MODEL PARAMETERS; NEW APPROACHES; PARAMETRIZATIONS; REGRESSION MODEL;

ALGORITHMS; ITERATIVE METHODS; MATHEMATICAL MODELS; NEURAL NETWORKS; REGRESSION ANALYSIS;

PARAMETER ESTIMATION;

EID: 77649238644     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, B, 39(1): 1-38, 1977.
2. B. Krishnapuram, L. Carin, M.A.T. Figueiredo and A.J. Hartemink, Sparse multinomial logistic regression: fast algorithms and generalization bounds, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6): 957-968, June 2005.
3. G. J. McLachlan and T. Krishnan, The EM algorithm and extensions, Wiley series in probability and statistics, New York, 1997.
4. G. Schwarz, Estimating the dimension of a model, Annals of Statistics, 6: 461-464, 1978.
5. K. Chen, L. Xu and H. Chi, Improved learning algorithms for Mixture of Experts in multiclass classification, IEEE Transactions on Neural Networks, 12(9): 1229-1252, November 1999.
6. M. Fridman, Hidden Markov Model Regression, Technical Report, Institute of mathematics, University of Minnesota, December 1993.
7. M. I. Jordan and R. A. Jacobs, Hierarchical Mixtures of Experts and the EM algorithm, Neural Computation, 6: 181-214, 1994.
8. P. Green. Iteratively Reweighted Least Squares for Maximum Likelihood Estimation, and some robust and resistant alternatives, Journal of the Royal Statistical Society, B, 46(2): 149-192, 1984.
9. R. E. Quandt and and J. B. Ramsey, Estimating mixtures of normal distributions and switching regressions, Journal of the American Statistical Association, 73(364): 730-752, 1978.
10. S. R. Waterhouse, Classification and regression using Mixtures of Experts, PhD thesis, Department of Engineering, Cambridge University, 1997.
11. V. E. McGee and W. T. Carleton, Piecewise regression, Journal of the American Statistical Association, 65, 1109-1124, 1970.