Chapter 14 References
·
Avilo Garcez, Broda K., Gabbay D., Symbolic knowledge extraction
from trained neural networks. Artificial Intelligence 2001; 125(1): 153-205.
·
Bishop C. M., Neural Network for Pattern Recognition. Oxford
University Press, 1995.
·
Breidbach O., Holthausen K., Scheidt B., Frenzel J., Analysis of
EEG data room in sud-den infant death risk patients. Theory Bioscience 1998;
117: 377-392.
·
Brodley C., Utgoff P., Multivariate decision trees. Machine
Learning 1995; 19(11):45-77.
·
Brunner D., Vasko R., Detka C., Monahan J., Reynolds C., and
Kupfer D., Muscle arti-facts in the sleep EEG: Automated detection and effect on
all-night EEG power spectra. Journal of Sleep Research 1996; 5:155–164.
·
Duke D., Nayak K. The EEG data, Florida State University.
Retrieved June 2002 from
http://www.scri.fsu.edu/~nayak/chaos/data.html
·
Duda R.O., Hart P. E., Pattern Classification. Wiley Interscience,
2000.
·
Fahlman S.E., Lebiere C., The cascade-correlation learning
architecture. In Advances in Neural Information Processing Systems II, ed. David
Touretzky: Morgan Kaufmann Publishers Inc., 1990.
·
Farlow S., Self-organizing Methods in Modeling: GMDH-Type
Algorithms. Marcel Dekker Inc., 1984.
·
Frean M.A., Thermal perceptron learning rule. Neural Computational
1992; 4: 946-957.
·
Galant S., Neural Network Learning and Expert Systems. MIT Press,
1993.
·
Iba H., deGaris, H., Sato T., Genetic programming with local
hill-climbing. Proceedings of the Third International Conference on Parallel
Problem Solving from Nature; 1994 October 9-14; Jerusalem; Berlin: Springer-Verlag,
1994.
·
Ishikawa M., Rule extraction by successive regularization. Neural
Networks 2000; 13:1171-1183.
·
Kovalerchuk B., Vityaev E., Data Mining in Finance: Advances in
Relational and Hybrid methods. Kluwer Academic Publishers, Boston, London,
Dordrecht, 2000.
·
Madala H., Ivakhnenko A., Inductive Learning Algorithms for
Complex Systems Modeling. CRC Press Inc., 1994.
·
Müller J.A., Lemke F., Self-Organizing Data Mining: Extracting
Knowledge From Data. Canada: Trafford Publishing, 2003.
·
Morik K., Imhoff M., Brockhausen P., Joachims T., Gather U.,
Knowledge discovery and knowledge validation in intensive care. Artificial
Intelligence in Medicine 2001; 19(3):225-49.
·
Parekh R., Yang J., Honavar V., Constructive Neural Network
Learning Algorithms for Pattern Classification. IEEE Transactions on Neural
Networks 2000; 11(2): 436-51.
·
Quinlan J., C4.5: Programs for Machine Learning. Morgan Kaufmann,
1993.
·
Salzberg S., Delcher A., Fasman K., Henderson J., A decision tree
system for finding genes in DNA. Computational Biology 1998; 5: 667-680.
·
Schetinin V, Schult J., A Combine Technique for Recognizing
Artifacts in the Electroencephalograms of Sleeping Newborns. IEEE Information
Technology in Biomedicine, to be published 2004.
·
Setiono R.,
Generating concise and
accurate classification rules for breast cancer diagnosis. Artificial
Intelligence in Medicine 2000; 18:205-219.
·
Sethi I., Yoo J., Structure-Driven Induction of Decision Tree
Classifiers Through Neural Learning. Pattern Recognition 1997: 30(11):1893-1904.
·
Tempo R.,
Calafiore G.,
Dabbene F., Randomized Algorithms for Analysis and Control of
Uncertain Systems. Springer Verlag 2003.
·
Towell G., Shavlik J., The extraction of refine rules from
knowledge based neural networks. Machine Learning 1993; 13:71-101.