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8. Cover T.M. [1965], Geometrical and statistical properties of systems of lin-
ear inequalities with applications in pattern recognition,
IEEE Transactions on
Electronic Computers
, 14, pp 326-334
9. Draper N.R., Smith H. [1998],
Applied regression analysis
, John Wiley & Sons
10. Duprat A., Huynh T., Dreyfus G. [1998], Towards a principled methodology for
neural network design and performance evaluation in QSAR; application to the
prediction of LogP,
Journal of Chemical Information and Computer Sciences
,
38, pp 586-594
11. Dreyfus C., Dreyfus G. [2003], A machine-learning approach to the estima-
tion of the liquidus temperature of glass-forming oxide blends,
Journal of Non-
Crystalline Solids
, 318, pp 63-78
12. Hampshire J.B., Pearlmutter B. [1990], Equivalence proofs for multilayer per-
ceptron classifiers and the Bayesian discriminant function,
Proceedings of the
1990 connectionist models summer school
, pp 159-172, Morgan Kaufmann
13. Hansch C., Leo A. [1995],
Exploring QSAR, Fundamentals and applications in
chemistry and biology
; American Chemical Society
14. Ho E., Kashyap R.L. [1965], An algorithm for linear inequalities and its appli-
cations,
IEEE Transactions on Electronic Computers
, 14, pp 683-688
15. Hopfield J.J. [1987], Learning algorithms and probability distributions in feed-
forward and feedback neural networks,
Proceedings of the National Academy of
Sciences
, 84, pp 8429-433
16. Hornik K., Stinchcombe M., White H. [1989], Multilayer feedforward networks
are universal approximators,
Neural Networks
, 2, pp 359-366
17. Hornik K., Stinchcombe M., White H. [1990], Universal approximation of an
unknown mapping and its derivatives using multilayer feedforward networks,
Neural Networks
, 3, pp 551-560
18. Hornik K. [1991], Approximation capabilities of multilayer feedforward net-
works,
Neural Networks
, 4, pp 251-257
19. Kim S.S., Sanders T.H. Jr. [1991], Thermodynamic modeling of phase diagrams
in binary alkali silicate systems,
Journal of the American Ceramics Society
, 74,
pp 1833-1840
20. Knerr S., Personnaz L., Dreyfus G. [1990], Single-layer learning revisited: a
stepwise procedure for building and training a neural network,
Neurocomputing:
algorithms, architectures and applications
, pp 41-50, Springer
21. Knerr S. [1991],
Un methode nouvelle de creation automatique de reseaux de
neurones pour la classification de donnees: application a la reconnaissance de
chiffres manuscrits
,These de Doctorat de l'Universite Pierre et Marie Curie,
Paris
22. Knerr S., Personnaz L., Dreyfus G. [1992], Handwritten digit recognition by
neural networks with Single-layer Training,
IEEE Transactions on Neural Net-
works
, 3, pp 962-968
23. LeCun Y., Boser B., Denker J.S., Henderson D., Howard R.E., Hubbard W.,
Jackel L.D. [1989], Backpropagation applied to handwritten zip code recogni-
tion,
Neural Computation
, 1, pp 541-551
24. Mallat S. [1989], A theory for multiresolution signal decomposition: the wavelet
transform,
IEEE Transactions on Pattern Analysis and Machine Intelligence
,
11, pp 674-693
25. McCulloch W.S., Pitts W. [1943], A logical calculus of the ideas immanent in
nervous activity,
Bulletin of Mathematical Biophysics
, 5, pp 115-133
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