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2.5 Collins, E., Glosh, S., and Scofield, C., Neural network decision learning system
applied to risk analysis: Mortgage underwriting and delinquency risk assess-
ment, in
DARPA Neural Network Study Final Report - Appendix E, Technical
Report 840
, pp. 65-79. Lincoln Laboratory, Massachusetts Institute of Technol-
ogy, Lexington, MA, March 1989.
2.6 Cooper, L. N., Elbaum, C., Reilly, D. L., and Scofield, C. L., Parallel, multi-
unit, adaptive, nonlinear pattern class separator and identifier, in
United States
Patents, Patent Number: 4,760,604
, July 1988.
2.7 Quadrillion Corporation, Q-Yield,
http://www.quadrillion.com
, 2002.
2.8 Devijver, P. A., and Kittler, J., On the edited nearest neighbor rule, in
Proc.
5th International Conference on Pattern Recognition
, vol. 1, pp. 72-80, Dec.
1980.
2.9 Dzwinel, W., How to make Sammon's mapping useful for multidimensional
data structure analysis, in
Pattern Recognition
, 27(7), Elsevier Science Ltd,
pp. 949-59, 1994.
2.10 Eberhardt, M., Hecht, R., and Konig, A., Einsatz des Konzepts
Machine-in-
the-Loop-Learning
zum individuellen, robusten Anlernen von Laborrobotersys-
temen, in
KI-Zeitschrift
, No. 2, pages 44-7, 2002.
2.11 Eberhardt, M., Kossebau, F. K. H., and Konig, A., Automatic feature selection
by genetic algorithms, in
Proc. Int Conf. on Artificial Neural Networks and
Genetic Algorithms, ICANNGA01
, pages 256-9, Prague, April 2001.
2.12 Fukunaga, K.,
Introduction to Statistical Pattern Recognition
. Academic
Press, Harcourt Brace Jovanovich, Publishers, Boston, San Diego, New York,
London, Sydney, Tokyo, Toronto, 1990.
2.13 Gates, G. W., The reduced nearest neighbour rule, in
IEEE Transactions on
Information Theory
, vol. IT-18, pp. 431-3, 1972.
2.14 Goser, K., Marks, K. M., Ruckert, U., and Tryba, V., Selbstorganisierende
Karten zur Prozessuberwachung und -voraussage, in
Proc. 3. Int. GI-Kongress
uber wissensbasierte Systeme, Munchen (16.-17. Okt.)
, pp. 225-37. Informatik
Fachberichte Nr. 227, Berlin: Springer Verlag, 1989.
2.15 Katayama, R., Watanabe, M., Kuwata, K., Kajitani, Y., and Nishida, Y., Per-
formance of self-generating radial basis function for function approximation, in
Proc. International Joint Conference on Neural Networks IJCNN'93
, Nagoya,
Japan, Vol.I, pp. 471-4, IEEE, 1993.
2.16 Kittler, J.,
Feature Selection and Extraction
, Academic Press, Inc.,
Tzai. Y. Young, King Sun-Fu, Publishers, Orlando, San Diego, New York,
Austin, London, Montreal, Sydney, Tokyo, Toronto, 1986.
2.17 Kober, R., Howard, C., and Bock, P., Anomaly detection in video images, in
Proc. 5th International Conference on Neural Networks and Their Applications
NEURO-NIMES'92
, 1992.
2.18 Kohler, C., Konig, A., Temelkova-Kurktschiev, T., and Hanefeld, M., Appli-
cation of interactive multivariate data visualisation to the analysis of patients
findings in metabolic research, in
Proc. 3rd Int. Conf. on Knowledge-Based
Intelligent Information Engineering Systems KES'99
, pp. 397-402, Adelaide,
Australia, Aug. 1999.
2.19 Kohonen, T.,
Self-Organization and Associative Memory
, Springer-Verlag,
Berlin, Heidelberg, London, Paris, Tokyo, Hong Kong, 1989.
2.20 Konig, A., Neuronale Strukturen zur sichtgestutzten Oberflacheninspektion
von Objekten in industrieller Umgebung, Darmstadter Dissertation D 17 (avail-
able from
http://www.iee.et.tu-dresden.de/koeniga
), Sept. 1995.
2.21 Konig, A., A novel supervised dimensionality reduction technique by feature
weighting for improved neural network classifier learning and generalization, in
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