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Introduction to computation and learning in artificial neural networks

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  • Masson, Egill
  • Wang, Yih-Jeou

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  • Masson, Egill & Wang, Yih-Jeou, 1990. "Introduction to computation and learning in artificial neural networks," European Journal of Operational Research, Elsevier, vol. 47(1), pages 1-28, July.
    • Handle: RePEc:eee:ejores:v:47:y:1990:i:1:p:1-28
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    1. Hachicha, Wafik & Ammeri, Ahmed & Masmoudi, Faouzi & Chachoub, Habib, 2010. "A comprehensive literature classification of simulation optimisation methods," MPRA Paper 27652, University Library of Munich, Germany.
    2. Balakrishnan, Nagraj & Chakravarty, Amiya K. & Ghose, Sanjoy, 1997. "Role of design-philosophies in interfacing manufacturing with marketing," European Journal of Operational Research, Elsevier, vol. 103(3), pages 453-469, December.
    3. Sabuncuoglu, Ihsan & Gurgun, Burckaan, 1996. "A neural network model for scheduling problems," European Journal of Operational Research, Elsevier, vol. 93(2), pages 288-299, September.
    4. Malavasi, Gabriele & Ricci, Stefano, 2001. "Simulation of stochastic elements in railway systems using self-learning processes," European Journal of Operational Research, Elsevier, vol. 131(2), pages 262-272, June.
    5. Reis dos Santos, M. Isabel & Porta Nova, Acacio M.O., 2006. "Statistical fitting and validation of non-linear simulation metamodels: A case study," European Journal of Operational Research, Elsevier, vol. 171(1), pages 53-63, May.
    6. Li, Hongtao & Bai, Juncheng & Li, Yongwu, 2019. "A novel secondary decomposition learning paradigm with kernel extreme learning machine for multi-step forecasting of container throughput," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    7. Shouhong Wang, 1996. "Nonparametric econometric modelling: A neural network approach," European Journal of Operational Research, Elsevier, vol. 89(3), pages 581-592, March.
    8. Randall S. Sexton & Naheel A. Sikander, 2001. "Data mining using a genetic algorithm‐trained neural network," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(4), pages 201-210, December.
    9. Klein, B. D. & Rossin, D. F., 1999. "Data quality in neural network models: effect of error rate and magnitude of error on predictive accuracy," Omega, Elsevier, vol. 27(5), pages 569-582, October.
    10. Sexton, Randall S. & McMurtrey, Shannon & Cleavenger, Dean, 2006. "Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem," European Journal of Operational Research, Elsevier, vol. 168(3), pages 1009-1018, February.
    11. Chen, S. K. & Mangiameli, P. & West, D., 1995. "The comparative ability of self-organizing neural networks to define cluster structure," Omega, Elsevier, vol. 23(3), pages 271-279, June.
    12. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    13. Shouhong Wang, 2001. "Cluster analysis using a validated self‐organizing method: cases of problem identification," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(2), pages 127-138, June.
    14. Vukadinovic, Katarina & Teodorovic, Dusan & Pavkovic, Goran, 1997. "A neural network approach to the vessel dispatching problem," European Journal of Operational Research, Elsevier, vol. 102(3), pages 473-487, November.
    15. Shouhong, Wang, 1998. "An Insight Into the Standard Back-propagation Neural Network Model for Regression Analysis," Omega, Elsevier, vol. 26(1), pages 133-140, February.
    16. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.

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