Neural discriminant analysis

IEEE Transactions on Neural Networks

Volumn 11, Issue 6, 2000, Pages 1394-1401

  Tsujitani, Masaaki ^a   Koshimizu, Takashi ^b  

^a OSAKA ELECTRO COMMUNICATION UNIVERSITY   (Japan)

^b BAYER YAKUHIN LTD   (Japan)

Author keywords

Bias correction; Binary data; Bootstrap methods; Cross entropy; Goodness of flt test; Residual analysis

Indexed keywords

EID: 7244237770     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.883460     Document Type: Article

References (26)

1. H. White, "Learning in artificial neural networks: A statistical perspective," Neural Comput., vol. 1, pp. 425-464, 1989.
2. H. Gish, "Maximum likelihood training of neural networks," in Artificial Intelligence Frontiers in Statistics, D. J. Hand, Ed. London, U.K.: Chapman & Hall, 1993, pp. 241-255.
3. B.D. Ripley, "Neural networks and related methods for classification," J. Roy. Statist. Soc., ser. B, vol. 56, pp. 409-456, 1994.
4. _, Pattern Recognition and Neural Networks. Cambridge, U.K.: Cambridge Univ. Press, 1996.
5. H. Ney, "On the probabilistic interpretation of neural network classifiers and discriminative training criteria," IEEE Trans. Pattern Anal. Machine Intell., vol. 17, pp. 107-119, 1995.
6. J. D. Paola and R. A. Schowengerdt, "A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification," IEEE Trans. Geosci. Remote Sensing, vol. 33, pp. 981-996, 1995.
7. H. Akaike, "Information theory and an extension of the maximum likelihood principle," in 2nd Int. Symp. Inform. Theory, B. N. Petrov, F. Caski, and A. Kiado, Eds., Budapest, 1973, pp. 267-281.
8. N. Murata, S. Yoshizawa, and S. Amari, "Network information criterion - Determining the number of hidden units for artificial neural network models," IEEE Trans. Neural Networks, vol. 5, pp. 865-872, 1994.
9. M. Ishiguro, Y. Sakamoto, and G. Kitagawa, "Bootstrapping log likelihood and EIC, an extension of AIC," Ann. Inst. Statist. Math., vol. 49, pp. 411-434, 1997.
10. D. Collett, Modeling Binary Data. London, U.K.: Chapman & Hall, 1991.
11. D. W. Hosmer and S. Lemeshow, Applied Logistic Regression. New York: Wiley, 1989.
12. J. M. Landwehr, D. Pregibon, and A. C. Shoemaker, "Graphical methods for assessing logistic regression models," J. Amer. Statist. Assoc., vol. 79, pp. 61-71, 1984.
13. B. Efron, "Estimating the error rate of a prediction rule: Improvement on cross-validation," J. Amer. Statist. Assoc., vol. 78, pp. 316-331, 1983.
14. G. Gong, "Cross-validation, the jackknife, and the bootstrap: Excess error estimation in forward logistic regression," J. Amer. Statist. Assoc., vol. 81, pp. 108-113, 1986.
15. M.-C. Wang, "Resampling procedures for reducing bias or error rate estimation in multinomial classification," Comput. Statist. Data Anal., vol. 4, pp. 15-39, 1986.
16. S. Konishi and M. Honda, "Comparison of procedures for estimation of error rates in discriminant analysis under nonnormal populations," J. Statist. Comp. Simul., vol. 36, pp. 105-115, 1990.
17. D. E. Jennings, "Outliers and residual distributions in logistic regression," J. Amer. Statist. Assoc., vol. 81, pp. 987-990, 1986.
18. C. M. Bishop, Neural Networks for Pattern Recognition. Oxford, U.K.: Clarendon, 1995.
19. J. B. Hampshire and B. A. Pelmutter, "Equivalence proofs for multilayer perceptron classifiers and the Bayesian discriminant function," in Proc. 1990 Connectionist Models Summer School, 1990, pp. 159-172.
20. G. E. Hinton, "Connectionist learning procedure, "Artificial Intell., vol. 40, pp. 185-234, 1989.
21. J. J. Hopfield, "Learning algorithms and probability distributions in feedforward and feedback networks," in Proc. Nat. Academy Sci., 1987, pp. 8429-8433.
22. S. A. Solla, E. Levin, and M. Fleisher, "Accelerated learning in layered neural networks," Complex Syst., vol. 2, pp. 625-640, 1988.
23. A. V. Ooyen and B. Nienhuis, "Improving the convergence of backpropagation algorithm," Neural Networks, vol. 5, pp. 465-471, 1992.
24. R. Tibshirani, "A comparison of some error estimates for neural network models," Neural Comput., vol. 8, pp. 152-163, 1996.
25. G. J. MacLachlan, "On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture," Appl. Statist., vol. 36, pp. 318-324, 1987.
26. J. M. Chambers and T. J. Hastie, Statistical Models in S. Pacific Grove, CA: Wadsworth and Brooks/Cole, 1992.