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A New ANN Training Approach for Efficiency Evaluation

Year 2010, Volume: 39 Issue: 3, 439 - 447, 01.03.2010

References

  • Athanassopoulos, A. D. and Curram, S. P. A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units, Journal of the Operational Research Society 47 8, 1000–1016, 1996.
  • Beirlant, J., Goegebeur, Y., Teugels, J., Segers, J., De Waal, D. and Ferro C. Statistics of Extremes: Theory and Applications(John Wiley and Sons, 2004).
  • Charnes, A., Cooper, W. W. and Rhodes, E. Measuring the efficiency of decision making units, European Journal of Operational Research 2 (6), 429–444, 1978.
  • Costa, A. and Markellos, R. N. Evaluating public transport efficiency with neural network models, Transportation Research C 5 (5), 301–312, 1997.
  • Delgado, F. J. Measuring efficiency with neural networks. An application to the public sec- tor, Economics Bulletin 3 (15), 1–10, 2005.
  • Emrouznejad, A., Parker, B. R. and Tavares, G. Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA, Socio-Economic Planning Sciences 42, 151–157, 2008.
  • Farrell, M. J. The Measurement of Productive Efficiency, Journal of the Royal Statistical Society 120, 253–281, 1957.
  • Gumbel, E. Statistics of Extremes (Columbia University Press, New York, 1958).
  • Santin D., Delgado, F. J. and Valino A. The measurement of technical efficiency: A neural network approach, Applied Economics 36, 627–635, 2004.
  • Santin, D. On the approximation of production functions: A comparison of artificial neural networks frontiers and efficiency techniques, Applied Economic Letters 15 (8), 597–600, 2008.
  • Shale, E. A., Athanassopoulos, A. D and Curram, S. P. A frontier-based neural network for assessing the efficiency of activity units, Foundations of Computing and Decision Sciences , Forthcoming.
  • Silverman, B. W. Density Estimation for Statistics and Data Analysis (Chapman and Hall, London, 1986).
  • Veelenturf, L. P. J. Analysis and Applications of Neural Networks (Prentice Hall, UK, 1995). [14] Zhang, X. S. Neural Networks in Optimization (Springer, 2000).

A New ANN Training Approach for Efficiency Evaluation

Year 2010, Volume: 39 Issue: 3, 439 - 447, 01.03.2010

References

  • Athanassopoulos, A. D. and Curram, S. P. A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units, Journal of the Operational Research Society 47 8, 1000–1016, 1996.
  • Beirlant, J., Goegebeur, Y., Teugels, J., Segers, J., De Waal, D. and Ferro C. Statistics of Extremes: Theory and Applications(John Wiley and Sons, 2004).
  • Charnes, A., Cooper, W. W. and Rhodes, E. Measuring the efficiency of decision making units, European Journal of Operational Research 2 (6), 429–444, 1978.
  • Costa, A. and Markellos, R. N. Evaluating public transport efficiency with neural network models, Transportation Research C 5 (5), 301–312, 1997.
  • Delgado, F. J. Measuring efficiency with neural networks. An application to the public sec- tor, Economics Bulletin 3 (15), 1–10, 2005.
  • Emrouznejad, A., Parker, B. R. and Tavares, G. Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA, Socio-Economic Planning Sciences 42, 151–157, 2008.
  • Farrell, M. J. The Measurement of Productive Efficiency, Journal of the Royal Statistical Society 120, 253–281, 1957.
  • Gumbel, E. Statistics of Extremes (Columbia University Press, New York, 1958).
  • Santin D., Delgado, F. J. and Valino A. The measurement of technical efficiency: A neural network approach, Applied Economics 36, 627–635, 2004.
  • Santin, D. On the approximation of production functions: A comparison of artificial neural networks frontiers and efficiency techniques, Applied Economic Letters 15 (8), 597–600, 2008.
  • Shale, E. A., Athanassopoulos, A. D and Curram, S. P. A frontier-based neural network for assessing the efficiency of activity units, Foundations of Computing and Decision Sciences , Forthcoming.
  • Silverman, B. W. Density Estimation for Statistics and Data Analysis (Chapman and Hall, London, 1986).
  • Veelenturf, L. P. J. Analysis and Applications of Neural Networks (Prentice Hall, UK, 1995). [14] Zhang, X. S. Neural Networks in Optimization (Springer, 2000).
There are 13 citations in total.

Details

Primary Language Turkish
Journal Section Mathematics
Authors

Sabri Erdem This is me

İpek Deveci Kocakoc This is me

Publication Date March 1, 2010
Published in Issue Year 2010 Volume: 39 Issue: 3

Cite

APA Erdem, S., & Kocakoc, İ. D. (2010). A New ANN Training Approach for Efficiency Evaluation. Hacettepe Journal of Mathematics and Statistics, 39(3), 439-447.
AMA Erdem S, Kocakoc İD. A New ANN Training Approach for Efficiency Evaluation. Hacettepe Journal of Mathematics and Statistics. March 2010;39(3):439-447.
Chicago Erdem, Sabri, and İpek Deveci Kocakoc. “A New ANN Training Approach for Efficiency Evaluation”. Hacettepe Journal of Mathematics and Statistics 39, no. 3 (March 2010): 439-47.
EndNote Erdem S, Kocakoc İD (March 1, 2010) A New ANN Training Approach for Efficiency Evaluation. Hacettepe Journal of Mathematics and Statistics 39 3 439–447.
IEEE S. Erdem and İ. D. Kocakoc, “A New ANN Training Approach for Efficiency Evaluation”, Hacettepe Journal of Mathematics and Statistics, vol. 39, no. 3, pp. 439–447, 2010.
ISNAD Erdem, Sabri - Kocakoc, İpek Deveci. “A New ANN Training Approach for Efficiency Evaluation”. Hacettepe Journal of Mathematics and Statistics 39/3 (March 2010), 439-447.
JAMA Erdem S, Kocakoc İD. A New ANN Training Approach for Efficiency Evaluation. Hacettepe Journal of Mathematics and Statistics. 2010;39:439–447.
MLA Erdem, Sabri and İpek Deveci Kocakoc. “A New ANN Training Approach for Efficiency Evaluation”. Hacettepe Journal of Mathematics and Statistics, vol. 39, no. 3, 2010, pp. 439-47.
Vancouver Erdem S, Kocakoc İD. A New ANN Training Approach for Efficiency Evaluation. Hacettepe Journal of Mathematics and Statistics. 2010;39(3):439-47.