Discrimination et régression par une méthode neuromimétique et par la méthode de segmentation CART : application à différentes données et comparaison des résultats

J.-P. Nakache; J. Vilain; B. Fertil

Revue de Statistique Appliquée (1996)

  • Volume: 44, Issue: 4, page 19-40
  • ISSN: 0035-175X

How to cite


Nakache, J.-P., Vilain, J., and Fertil, B.. "Discrimination et régression par une méthode neuromimétique et par la méthode de segmentation CART : application à différentes données et comparaison des résultats." Revue de Statistique Appliquée 44.4 (1996): 19-40. <http://eudml.org/doc/106403>.

author = {Nakache, J.-P., Vilain, J., Fertil, B.},
journal = {Revue de Statistique Appliquée},
language = {fre},
number = {4},
pages = {19-40},
publisher = {Société de Statistique de France},
title = {Discrimination et régression par une méthode neuromimétique et par la méthode de segmentation CART : application à différentes données et comparaison des résultats},
url = {http://eudml.org/doc/106403},
volume = {44},
year = {1996},

AU - Nakache, J.-P.
AU - Vilain, J.
AU - Fertil, B.
TI - Discrimination et régression par une méthode neuromimétique et par la méthode de segmentation CART : application à différentes données et comparaison des résultats
JO - Revue de Statistique Appliquée
PY - 1996
PB - Société de Statistique de France
VL - 44
IS - 4
SP - 19
EP - 40
LA - fre
UR - http://eudml.org/doc/106403
ER -


  1. Breiman L., Friedman J.H., Ohlsen R.A., Stone C.J., Classification and Regression Trees. Belmont, 1984. Zbl0541.62042
  2. Burke H.B., Rosen D.B., Goodman P.H., Comparing Artificial neural networks to other statistical methods for medical outcome prodiction. In proceeding : IEEE Int. Conference on Neural Networks, Orlando, F1, p. 2213, 1994. 
  3. CART, A software classification and regression trees. Yorshire Ct. Lafayette, California: California Statistical Software, inc., 1984. MR726392
  4. Celeux J.P., Nakache J.-P., Analyse discriminante sur variables qualitatives. Polytechnica Ed, 1994. 
  5. Cichocki A., Unbehauen R., Neural Networks for Optimization and Signal Processing, p. 526. Stuttgart: Wiley, J. & Sons Ltd & Teubner, B.G., 1993. Zbl0824.68101
  6. Coustere C., Fertil B., Un nouvel outil pour l'analyse de données : les réseaux de neurones. Applications en microbiologie, Bulletin de la société Française de Microbiologie7, 10, 1992. 
  7. Davalo E., Naim P., Des réseaux de neurones, 2 édition, p. 232. Paris: Editions Eyrolles, 1993. 
  8. Fertil B., Vilain J., Multiple Learning Sessions to Improve Predictions and Evaluate Reliability of Neural Networks. In proceeding : 4th International conference on Artificial Neural Networks, Sorrento, Italy, 1994, p. 1323. 
  9. Flamant Y., Lacaine F., Hay J.M., Maillard J.N., Syndromes douloureux aigus de l'abdomen. Aide au diagnostic par ordinateur, Nouvelle Presse Médicale10, 3367, 1981. 
  10. Gallinari P., Thiria S., Badran F., Fogelman-Soulie F., On the relations between discriminant analysis and multilayer perceptrons, in Neural Networks (USA), vol. 4, n°3, p. 349 -69, 1991. 
  11. Gernoth K.A., Clark J.W., Neural networks that learn to predict probabilities : Global models of nuclear stability and decay, Neural Networks8, 291, 1995. 
  12. Gueguen A., Nakache J.-P., Méthode de discrimination basée sur la construction d'un arbre de décision binaire, Rev. Stat. Appl., 36, 19, 1988. 
  13. Gueguen A., Nicolau J., Nakache J.-P., Utilisation des réseaux probabilistes en analyse discriminante sur variables qualitatives, Rev. Stat. Appl., 1996. 
  14. Harrison D., Rubinfeld D.L., Hedonic prices and the demand for clean air, J. Envir. Econ. and Management5, 81, 1978. Zbl0375.90023
  15. Jepson B., Collins A., Evans A., Post-neural network procedure to determine expected prediction values and their confidence limits, Neural Computing & applications1, 224, 1993. 
  16. Kass G.V., An exploratory technique for investigating large quantities of categorical data, Applied Statistics, 29, 119, 1980. 
  17. Katz A.S., Katz S., Lowe N., Fundamentals of the bootstrap based analysis of neural network's accuracy. In proceeding : WCNN, San Diego, USA, 1994, p. 673. 
  18. Le Cun Y., Learning Scheme for asymmetric threshold networks. In proceeding : Cognitiva 85, Paris, France, p. 599, 1985. 
  19. Leon M.A., Binary response forecasting : comparaison between neural networks and logistic regression analysis, in proc. of WCN 2, 244, 1994. 
  20. Liu Y., Unbiased Estimate of Generalization Error and Model Selection in Neural Network, Neural Networks8, 215, 1995. 
  21. Martin C.E., Rogers S.K., Ruck D.W., Neural network Bayes error estimation, in proc. of IEEE ICNN 305, 1994. 
  22. Mascioli F.M.F., Martinelli G., Lazzaro D., comparison of Constructive Algorithms for Neural Networks, in proc. of ICANN 1, 731, 1994. 
  23. Masters T., Practical neural network recipes in C++, p. 493. San Diego, CA, Academic Press, Inc, 1993. Zbl0818.68049MR1214791
  24. Masters T., Signal and image processing with neural networks, a C++ sourcebook. New York, Wiley & sons, inc., 1994. 
  25. Mcclelland J.L., Rumelhart D.E., Explorations in parallel distributed processing. A handbook of models, programs and exercises, p. 344. Cambridge, MA, MIT Press, 1988. 
  26. Michie D., Spiegelmalter D.J., Taylor C.C., Machine learning Neural and Statistical Classification, Ed. Ellis HorwoodN.Y., 1994. Zbl0827.68094
  27. Morgan J.A., Messenger R.C., A modal search technique for predictive nominal scale multivariate analysis, J. Amer. Statis. Ass.67, 768, 1972. 
  28. Morgan J.A., Sonquist J.N., Problems in the analysis of survey data and a proposal, J. Amer. Statist. Ass.58, 415, 1963. Zbl0114.10103
  29. Nakache J.P., Golmard J.L., Gueguen A., Comparison of the performance of the conditional independence based model and the CART tree-structured discrimination model applied to a large medical data set. In proceeding : MIE 93, Jérusalem (Israël), 1993. 
  30. Nix D.A., Weigend A.S., Estimating the Mean and Variance of the Target Probability Distribution, in proc. of IEEE ICNN 55, 1994. 
  31. Paass G., Assessing predictive accuracy by the bootstrap algorithm. In proceeding, 4th International conference on Artificial Neural Networks, Sorrento, Italy, p. 823, 1994. 
  32. Perantonis S.J., Karras D.A., An efficient constrained learning algorithm with momentum acceleration, Neural Networks8, 237, 1995. 
  33. Ripley B.D., Statistical aspects of neural networks. In : O.E. Barndorff-Nielsen, J.L. Jensen, and W.S. Kendall (ed.), Networks and Chaos — Statistical and Probabilistic Aspects, p. 40, Chapman & Hall, 1993. Zbl0825.68531
  34. Ripley B.D., Network methods in statistics. In : F.P. Kelly (ed.), Probability, Statistics, Optimisation, a Tribute to Peter Whittle, p. 241, Wiley, 1994a. Zbl0856.62007MR1320756
  35. Ripley B.D., Neural networks and flexible regression and discrimination. In : K.V. Mardia (ed.), Advances in Statistics2, p. 39. Carfax: Abingdon, 1994b. Zbl0815.62037
  36. Ruck D.W., Rogers S.K., Kabrisky M., Oxley M.E., Suter B.W., The multilayer perceptron as an approximation to a Bayes optimal discriminant function, IEEE Transactions on Neural Networks1, 296, 1990. 
  37. Rumelhart D.E., Hinton G.E., Williams R.J., Learning internal representations by error propagation. In : D. E. Rumelhart and J.L. McClelland (ed.), Parallel distributed processing : explorations in the microstructure of cognition, Vol. 1, Fondations, p. 318. Cambridge, MA, MIT Press, 1986. 
  38. Seroussi B., ARC et AURC, Comparison of discrimination methods; application to the acute abdominal pain diagnosis. In : D. Tfistsis (ed.), Lecture notes in médical informatics, Objective medical decision making, Vol. 28, p. 12: Springer-Verlag, 1985. 
  39. Siegel S., Nonparametric statistics for the behavioral sciences, McGraw-Hill Intern. Book Company, 1956. Zbl0071.35301
  40. Shadmehr R., D'Argenio D.Z., A comparison of a neural network based estimator and two statistical estimators in a sparse and noisy data environment, in proc. of IJCNN 1, 289, 1990. 
  41. SPAD.N., Système portable dAnalyse des Données - procédure NEURO. Saint-Mandé, CISIA, 1993. 
  42. SPAD.S., Système Portable d'Analyse des Données - Segmentation. Saint-Mandé: CISIA, 1993. 
  43. Srivastava A.N., Weigend A.S., Computing the probability density in connectionist regression, in proc. of WCNN 2, 311, 1994. 
  44. Weisbuch G., Dynamique des systèmes complexes, p. 212. Paris, InterEditions & Editions du CNRS, 1989. 

NotesEmbed ?


You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.


Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.