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Book Review: Scarborough, D., & Somers, M. J. (2006). Neural networks in organizational research: Applying pattern recognition to the analysis of organizational behavior. Washington, DC: American Psychological Association
Steven Walczak
University of Colorado-Denver and Health Sciences Center
References
- Detienne, K., Detienne, D., & Joshie, S. (2003). Neural networks as statistical tools for business researchers. Organizational Research Methods, 6, 236-265.[Abstract]
- Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks. Neural Networks, 4, 251-257.[CrossRef][Web of Science]
- Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359-366.[CrossRef][Web of Science]
- Smith, K.A., & Gupta, J.N.D. (2000). Neural networks in business: Techniques and applications for the operations researcher. Computers & Operations Research, 27, 1023-1044.
- Smith, M. (1993). Neural networks for statistical modeling. New York: Van Nostrand Reinhold.
- Warner, B., & Misra, M. (1996). Understanding neural networks as statistical tools. The American Statistician, 50, 284-293.[CrossRef]
- White, H. (1990). Connectionist nonparametric regression multilayer feedforward networks can learn arbitrary mappings. Neural Networks, 3, 535-549.[CrossRef][Web of Science]
- Wong, B.K., Lai, V.S., & Lam, J. (2000). A bibliography of neural network business applications research: 1994-1998. Computers & Operations Research, 27, 1045-1076.[CrossRef][Web of Science]
- Zahedi, F. (1996). A meta-analysis of financial applications of neural networks. International Journal of Computational Intelligence in Organizations, 1, 164-178.
This version was published on October
1, 2007
Organizational Research Methods, Vol. 10, No. 4,
710-712 (2007)
DOI: 10.1177/1094428107300338

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