Search Results - Omlin, C.W.

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  1. 1
    Impacts and cost of faults on services in cellular networks

    Impacts and cost of faults on services in cellular networks by Kogeda, O.P., Agbinya, J.I., Omlin, C.W.

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  2. 2
    Equivalence in knowledge representation: automata, recurrent neural networks, and dynamical fuzzy systems

    Equivalence in knowledge representation: automata, recurrent neural networks, and dynamical fuzzy systems by Giles, C.L., Omlin, C.W., Thornber, K.K.

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  3. 3
    Fuzzy finite-state automata can be deterministically encoded into recurrent neural networks

    Fuzzy finite-state automata can be deterministically encoded into recurrent neural networks by Omlin, C.W., Thornber, K.K., Giles, C.L.

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  4. 4
    A Probabilistic Approach To Faults Prediction in Cellular Networks

    A Probabilistic Approach To Faults Prediction in Cellular Networks by Kogeda, O.P., Agbinya, J.I., Omlin, C.W.

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  5. 5
    Haar features for FACS AU recognition

    Haar features for FACS AU recognition by Whitehill, J., Omlin, C.W.

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  6. 6
    Rule revision with recurrent neural networks

    Rule revision with recurrent neural networks by Omlin, C.W., Giles, C.L.

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  7. 7
    Local versus global segmentation for facial expression recognition

    Local versus global segmentation for facial expression recognition by Whitehill, J., Omlin, C.W.

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  8. 8
    Pruning recurrent neural networks for improved generalization performance

    Pruning recurrent neural networks for improved generalization performance by Omlin, C.W., Giles, C.L.

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  9. 9
    Rule extraction from recurrent neural networks using a symbolic machine learning algorithm

    Rule extraction from recurrent neural networks using a symbolic machine learning algorithm by Vahed, A., Omlin, C.W.

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  10. 10
    A hybrid system for signature verification

    A hybrid system for signature verification by Wessels, T., Omlin, C.W.

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  11. 11
    Pruning recurrent neural networks for improved generalization performance

    Pruning recurrent neural networks for improved generalization performance by Giles, C.L., Omlin, C.W.

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  12. 12
    What inductive bias gives good neural network training performance?

    What inductive bias gives good neural network training performance? by Snyders, S., Omlin, C.W.

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  13. 13
    Refining hidden Markov models with recurrent neural networks

    Refining hidden Markov models with recurrent neural networks by Wessels, T., Omlin, C.W.

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  14. 14
    Representation of fuzzy finite state automata in continuous recurrent, neural networks

    Representation of fuzzy finite state automata in continuous recurrent, neural networks by Omlin, C.W., Thornber, K.K., Giles, C.L.

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  15. 15
    Prediction of seismic events in mines using neural networks

    Prediction of seismic events in mines using neural networks by van Zyl, J., Omlin, C.W.

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  16. 16
    Inserting rules into recurrent neural networks

    Inserting rules into recurrent neural networks by Giles, C.L., Omlin, C.W.

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  17. 17
    Rule refinement with recurrent neural networks

    Rule refinement with recurrent neural networks by Giles, C.L., Omlin, C.W.

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  18. 18
    Heuristics for the extraction of rules from discrete-time recurrent neural networks

    Heuristics for the extraction of rules from discrete-time recurrent neural networks by Omlin, C.W., Giles, C.L., Miller, C.B.

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  19. 19
    Constructing deterministic finite-state automata in sparse recurrent neural networks

    Constructing deterministic finite-state automata in sparse recurrent neural networks by Omlin, C.W., Giles, C.L.

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  20. 20
    Training recurrent neural networks with temporal input encodings

    Training recurrent neural networks with temporal input encodings by Omlin, C.W., Giles, C.L., Horne, B.G., Leerink, L.R., Lin, T.

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