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Alex Aussem: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Alex Aussem, Jean-Marc Petit
    e-functional dependency inference: application to DNA microarray expression data. [Citation Graph (0, 0)][DBLP]
    BDA, 2002, pp:- [Conf]
  2. Alex Aussem, C. Boutevin
    Segmentation of switching dynamics with a Hidden Markov Model of neural prediction experts. [Citation Graph (0, 0)][DBLP]
    ESANN, 2001, pp:251-256 [Conf]
  3. Alex Aussem
    Closed Loop Stability of FIR-Recurrent Neural Networks. [Citation Graph (0, 0)][DBLP]
    ICANN, 2003, pp:523-529 [Conf]
  4. Alex Aussem
    Sufficient Conditions for Error Back Flow Convergence in Dynamical Recurrent Neural Networks. [Citation Graph (0, 0)][DBLP]
    IJCNN (4), 2000, pp:577-582 [Conf]
  5. Alex Aussem, Antoine Mahul, Raymond Marie
    Queuing Network Modeling with Distributed Neural Networks for Service Quality Estimation in B-ISDN Network. [Citation Graph (0, 0)][DBLP]
    IJCNN (5), 2000, pp:392-397 [Conf]
  6. Alex Aussem, Fionn Murtagh
    Combining Neural Network Forecasts on Wavelet-transformed Time Series. [Citation Graph (0, 0)][DBLP]
    Connect. Sci., 1997, v:9, n:1, pp:113-122 [Journal]
  7. Antoine Mahul, Alex Aussem
    Distributed Neural Networks for Quality of Service Estimation in Communication Networks. [Citation Graph (0, 0)][DBLP]
    International Journal of Computational Intelligence and Applications, 2003, v:3, n:3, pp:297-308 [Journal]
  8. Alex Aussem, Fionn Murtagh
    Web traffic demand forecasting using wavelet-based multiscale decomposition. [Citation Graph (0, 0)][DBLP]
    Int. J. Intell. Syst., 2001, v:16, n:2, pp:215-236 [Journal]
  9. Alex Aussem, Fionn Murtagh, Marc Sarazin
    Dynamical recurrent neural networks -- towards environmental time series prediction. [Citation Graph (0, 0)][DBLP]
    Int. J. Neural Syst., 1995, v:6, n:2, pp:145-170 [Journal]
  10. Alex Aussem
    Dynamical recurrent neural networks towards prediction and modeling of dynamical systems. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 1999, v:28, n:1-3, pp:207-232 [Journal]
  11. Alex Aussem, David Hill
    Neural-network metamodelling for the prediction of Caulerpa taxifolia development in the Mediterranean sea. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2000, v:30, n:1-4, pp:71-78 [Journal]
  12. Alex Aussem, Fionn Murtagh, Marc Sarazin
    Fuzzy astronomical seeing nowcasts with a dynamical and recurrent connectionist network. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 1996, v:13, n:2-4, pp:359-373 [Journal]
  13. Fionn Murtagh, G. Zheng, J. G. Campbell, Alex Aussem
    Neural network modelling for environmental prediction. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2000, v:30, n:1-4, pp:65-70 [Journal]
  14. Alex Aussem
    Sufficient Conditions for Error Backflow Convergence in Dynamical Recurrent Neural Networks. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2002, v:14, n:8, pp:1907-1927 [Journal]
  15. Alex Aussem, Sergio Rodrigues de Morais, Marilys Corbex
    Nasopharyngeal Carcinoma Data Analysis with a Novel Bayesian Network Skeleton Learning Algorithm. [Citation Graph (0, 0)][DBLP]
    AIME, 2007, pp:326-330 [Conf]

  16. Robust Gene Selection from Microarray Data with a Novel Markov Boundary Learning Method: Application to Diabetes Analysis. [Citation Graph (, )][DBLP]

  17. Graph-Based Analysis of Nasopharyngeal Carcinoma with Bayesian Network Learning Methods. [Citation Graph (, )][DBLP]

  18. A Conservative Feature Subset Selection Algorithm with Missing Data. [Citation Graph (, )][DBLP]

  19. Incremental Bayesian Network Learning for Scalable Feature Selection. [Citation Graph (, )][DBLP]

  20. Exploiting Data Missingness in Bayesian Network Modeling. [Citation Graph (, )][DBLP]

  21. A Novel Scalable and Data Efficient Feature Subset Selection Algorithm. [Citation Graph (, )][DBLP]

  22. An Efficient and Scalable Algorithm for Local Bayesian Network Structure Discovery. [Citation Graph (, )][DBLP]

  23. Analysis of Nasopharyngeal Carcinoma Data with a Novel Bayesian Network Learning Algorithm. [Citation Graph (, )][DBLP]

  24. A novel Bayesian Network structure learning algorithm based on minimal correlated itemset mining techniques. [Citation Graph (, )][DBLP]

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