A deterministic approach to small data set partitioning for neural networks

Advances in Business and Management Forecasting

Volumn 7, Issue , 2010, Pages 157-170

  Smith, Gregory E ^a   Ragsdale, Cliff T ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84884526791     PISSN: 14774070     EISSN: None     Source Type: Book Series    
DOI: 10.1108/S1477-4070(2010)0000007014     Document Type: Article

References (19)

1. Archer, N. P., & Wang, S. (1993). Application of the back propagation neural network algorithm with monotonicity constraints for two-group classification problems. Decision Sciences, 24(1), 60-75.
2. Burke, L. Ll. (1991). Introduction to artificial neural systems for pattern recognition. Computers and Operations Research, 18(2), 211-220.
3. Burke, L. L., & Ignizio, J. P. (1992). Neural networks and operations research: An overview. Computers and Operations Research, 19(3/4), 179-189.
4. Fausett, L. (1994). Fundamentals of neural networks: Architectures, algorithms, and applications. Upper Saddle River: Prentice Hall.
5. Glorfeld, L. W., & Hardgrave, B. C. (1996). An improved method for developing neural networks: The case of evaluating commercial loan creditworthiness. Computers and Operations Research, 23(10), 933-944.
6. Hoptroff, R., Bramson, M., & Hall, T. (1991). Forecasting economic turning points with neural nets. IEEE INNS International Joint Conference of Neural Networks, Seattle, WA, USA, pp. 347-352.
7. Hornick, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359-366.
8. Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis (6th ed.). Upper Saddle River: Prentice Hall.
9. Klimasauskas, C. C., Guiver, J. P., & Pelton, G. (1989). Neural works predict. Pittsburg: NeuralWare, Inc.
10. Manly, B. (2004). Multivariate statistical methods: A primer (3rd ed.). London: Chapman and Hall.
11. Markham, I. S., & Ragsdale, C. T. (1995). Combining neural networks and statistical predictions to solve the classification problem in discriminant analysis. Decision Sciences, 26, 229-242.
12. Patuwo, E., Hu, M. Y., & Hung, M. S. (1993). Two-group classification using neural networks. Decision Sciences, 24(4), 825-845.
13. Piramuthu, S., Shaw, M., & Gentry, J. (1994). A classification approach using multi-layered neural networks. Decision Support Systems, 11, 509-525.
14. Salchenberger, L. M., Cinar, E. M., & Lash, N. A. (1992). Neural networks: A new tool for predicting thrift failures. Decision Sciences, 23(4), 899-916.
15. Sexton, R. S., Sriram, R. S., & Etheridge, H. (2003). Improving decision effectiveness of artificial neural networks: A modified genetic algorithm approach. Decision Sciences, 34(3), 421-442.
16. Smith, K. A., & Gupta, J. N. D. (2000). Neural networks in business: Techniques and applications for the operations researcher. Computers and Operations Research, 27, 1023-1044.
17. Tam, K. Y., & Kiang, M. Y. (1992). Managerial applications of neural networks: The case of bank failure prediction. Management Science, 38(7), 926-947.
18. Wilson, R. L., & Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11, 545-557.
19. Zobel, C., Cook, D., & Ragsdale, C. (2006). Dynamic data driven classification using boundary observations. Decision Sciences, 37(2), 247-262.