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Christian W. Omlin: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Christian W. Omlin, C. Lee Giles
    Constructing Deterministic Finite-State Automata in Recurrent Neural Networks. [Citation Graph (1, 0)][DBLP]
    J. ACM, 1996, v:43, n:6, pp:937-972 [Journal]
  2. Andries Kruger, C. Lee Giles, Frans Coetzee, Eric J. Glover, Gary William Flake, Steve Lawrence, Christian W. Omlin
    DEADLINER: Building a New Niche Search Engine. [Citation Graph (0, 0)][DBLP]
    CIKM, 2000, pp:272-281 [Conf]
  3. Jacob Whitehill, Christian W. Omlin
    Local versus Global Segmentation for Facial Expression Recognition. [Citation Graph (0, 0)][DBLP]
    FG, 2006, pp:357-362 [Conf]
  4. Jacob Whitehill, Christian W. Omlin
    Haar Features for FACS AU Recognition. [Citation Graph (0, 0)][DBLP]
    FG, 2006, pp:97-101 [Conf]
  5. Christian W. Omlin, C. Lee Giles, Karvel K. Thornber
    Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective. [Citation Graph (0, 0)][DBLP]
    Hybrid Neural Systems, 1998, pp:123-143 [Conf]
  6. Okuthe P. Kogeda, Johnson I. Agbinya, Christian W. Omlin
    Impacts and Cost of Faults on Services in Cellular Networks. [Citation Graph (0, 0)][DBLP]
    ICMB, 2005, pp:551-555 [Conf]
  7. Christian W. Omlin, C. Lee Giles
    Training Second-Order Recurrent Neural Networks using Hints. [Citation Graph (0, 0)][DBLP]
    ML, 1992, pp:361-366 [Conf]
  8. Okuthe P. Kogeda, Johnson I. Agbinya, Christian W. Omlin
    A Probabilistic Approach To Faults Prediction in Cellular Networks. [Citation Graph (0, 0)][DBLP]
    ICN/ICONS/MCL, 2006, pp:130- [Conf]
  9. Dane Walsh, Christian W. Omlin
    Automatic Detection of Film Orientation with Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    IEA/AIE, 2002, pp:36-46 [Conf]
  10. Sean Snyders, Christian W. Omlin
    What Inductive Bias Gives Good Neural Network Training Performance? [Citation Graph (0, 0)][DBLP]
    IJCNN (3), 2000, pp:445-450 [Conf]
  11. T. Wessels, Christian W. Omlin
    Refining Hidden Markov Models with Recurrent Neural Networks. [Citation Graph (0, 0)][DBLP]
    IJCNN (2), 2000, pp:271-278 [Conf]
  12. T. Wessels, Christian W. Omlin
    A Hybrid System for Signature Verification. [Citation Graph (0, 0)][DBLP]
    IJCNN (5), 2000, pp:509-514 [Conf]
  13. Sean Snyders, Christian W. Omlin
    Inductive Bias in Recurrent Neural Networks. [Citation Graph (0, 0)][DBLP]
    IWANN (1), 2001, pp:339-346 [Conf]
  14. Jacobus van Zyl, Christian W. Omlin
    Knowledge-Based Neural Networks for Modelling Time Series. [Citation Graph (0, 0)][DBLP]
    IWANN (2), 2001, pp:579-586 [Conf]
  15. Christian W. Omlin, Sean Snyders
    Inductive bias strength in knowledge-based neural networks: application to magnetic resonance spectroscopy of breast tissues. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2003, v:28, n:2, pp:121-140 [Journal]
  16. Christian W. Omlin, C. Lee Giles
    Stable encoding of large finite-state automata in recurrent neural networks with sigmoid discriminants. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1996, v:8, n:4, pp:675-696 [Journal]
  17. A. Vahed, Christian W. Omlin
    A Machine Learning Method for Extracting Symbolic Knowledge from Recurrent Neural Networks. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2004, v:16, n:1, pp:59-71 [Journal]
  18. Christian W. Omlin, C. Lee Giles
    Extraction of rules from discrete-time recurrent neural networks. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1996, v:9, n:1, pp:41-52 [Journal]
  19. Christian W. Omlin, C. Lee Giles
    Rule Revision With Recurrent Neural Networks. [Citation Graph (0, 10)][DBLP]
    IEEE Trans. Knowl. Data Eng., 1996, v:8, n:1, pp:183-188 [Journal]
  20. R. Chandra, Christian W. Omlin
    Training and extraction of fuzzy finite state automata in recurrent neural networks. [Citation Graph (0, 0)][DBLP]
    Computational Intelligence, 2006, pp:274-279 [Conf]

  21. Knowledge Discovery using Artificial Neural Networks for a Conservation Biology Domain. [Citation Graph (, )][DBLP]


  22. Facial Action Unit Recognition Using Recurrent Neural Networks. [Citation Graph (, )][DBLP]


  23. Hybrid Recurrent Neural Networks: An Application to Phoneme Classification. [Citation Graph (, )][DBLP]


  24. Hybrid Evolutionary One-Step Gradient Descent for Training Recurrent Neural Networks. [Citation Graph (, )][DBLP]


  25. A Hybrid Recurrent Neural Networks Architecture Inspired by Hidden Markov Models: Training and Extraction of Deterministic Finite Automaton. [Citation Graph (, )][DBLP]


  26. The Comparison and Combination of Genetic and Gradient Descent Learning in Recurrent Neural Networks: An Application to Speech Phoneme Classification. [Citation Graph (, )][DBLP]


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