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A
M. Georgiopoulos, Gelenbe, E., DeMara, R., Gonzalez, A., Mollaghasemi, M., Wu, A., Russell, I., Anagnostopoulos, G., and Secretan, J., “Assessing and Evaluating our progress on the CRCD Experiences at the University of Central Florida: An NSF Project”, in 2006 ASEE, Chicago, Illinois, 2006.
M. Georgiopoulos, Gelenbe, E., DeMara, R., Gonzalez, A., Mollaghasemi, M., Wu, A., Russell, I., Anagnostopoulos, G., and Secretan, J., “Assessing and Evaluating our progress on the CRCD Experiences at the University of Central Florida: An NSF Project”, 2006.
B
A. Henninger, Gerber, W., DeMara, R., Georgiopoulos, M., and Gonzalez, A., “Behavior Modeling Framework for Embedded Simulation”, Interservice/Industry Training,Simulation & Education Conference (I/ITSEC 98), pp. 655–662, 1998.
C
A. Henninger, Gonzalez, A., Georgiopoulos, M., and DeMara, R., “A connectionist-symbolic approach to modeling agents: Neural networks grouped by contexts”, Proceedings of the CONTEXT-01 Conference, pp. 198–209, 2001.
M. Georgiopoulos, Russell, I., Castro, J., Wu, A., Kysilka, M., and DeMara, R., “A CRCD Experience: Integrating machine learning modules into introductory engineering and science programming courses”, Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition, 2003.
M. Georgiopoulos, Gelenbe, E., DeMara, R., Gonzalez, A., Kysilka, M., Mollaghasemi, M., Wu, A., Russell, I., Anagnostopoulos, G., and Secretan, J., “CRCD Experiences at the University of Central Florida: An NSF Project”, in the ASEE 2005 Annual Conference and Exposition, Portland Oregon, 2005.
M. Georgiopoulos, Castro, J., Gelenbe, E., DeMara, R., Gonzalez, A., and Kysilka, M., “CRCD Experiences at the University of Central Florida: An NSF Project”, Proceedings of the ASEE 2004 Annual Conference and Exposition, 2004.
M. Georgiopoulos, Castro, J., Wu, A., DeMara, R., Gelenbe, E., and Gonzalez, A., “CRCD in machine learning at the University of Central Florida preliminary experiences”, Proceedings of the 8th annual conference on Innovation and Technology in Computer Science Education, p. 249, 2003.
D
J. Castro, Georgiopoulos, M., DeMara, R., and Gonzalez, A., “A data partitioning approach to speed the Fuzzy ARTMAP algorithm using the Hilbert space-filling curve”, pp. 2367–2372, 2004.
J. Castro, Georgiopoulos, M., DeMara, R., and Gonzalez, A., “Data-partitioning using the Hilbert space filling curves Effect on the speed of convergence of Fuzzy ARTMAP for large database problems”, Neural Networks, vol. 18, pp. 967-984, 2005.
J. Castro, Georgiopoulos, M., DeMara, R., and Gonzalez, A., “Data-partitioning using the Hilbert space filling curves: Effect on the speed of convergence of Fuzzy ARTMAP for large database problems”, Neural Networks, vol. 18.7, pp. 967–984, 2005.
F
A. Henninger, Gonzalez, A., Gerber, W., Georgiopoulos, M., and DeMara, R., “On the Fidelity of SAFs: Can performance data help?”, Proceedings of the 2000 Interservice/Industry Training,Simulation and Education Conference (I/ITSEC-2000), pp. 147–154, 2000.
I
H. Bahr, Georgiopoulos, M., and DeMara, R., “Integer-encoded massively parallel processing of fast learning Fuzzy ARTMAP neural networks”, in Proceedings SPIE; Conference of Applications and Science of Artificial Neural Networks III, Orlando, FL, 1997.
L
H. K. Fernlund, Gonzalez, A. J., Georgiopoulos, M., and DeMara, R., “Learning tactical human behavior through observation of human performance”, Cybernetics–Part B: Cybernetics,Man,IEEE Transactions on Systems, vol. 36.1, pp. 128–140, 2006.
A. Henninger, Gonzalez, A., Georgiopoulos, M., and DeMara, R., “The Limitations of Static Performance Metrics for Dynamic Tasks Learned Through Observation”, Proceedings of the Tenth Conference on Computer Generated Forces and Behavioral Representation, pp. 147–154, 2001.
M
A. Henninger, Gonzalez, A., Georgiopoulos, M., and DeMara, R., “Modeling Semi-Automated Forces with Neural Networks: Performance Improvement through a Modular Approach”, 2000.
P
J. Castro, Georgiopoulos, M., Secretan, J., DeMara, R., Anagnostopoulos, G. C., and Gonzalez, A., “Parallelization of Fuzzy ARTMAP to improve its convergence speed: The network partitioning approach and the data partitioning approach”, Nonlinear Analysis: Theory,Methods and Applications, vol. 63.5-7, pp. e877–e889, 2005.
J. Castro, Georgiopoulos, M., DeMara, R., and Gonzalez, A., “A Partitioned Fuzzy ARTMAP implementation for fast processing of large databases on sequential machines”, 2004.
J. Castro, Secretan, J., Georgiopoulos, M., DeMara, R., Anagnostopoulos, G. C., and Gonzalez, A., “Pipelining of Fuzzy ARTMAP without Matchtracking: Correctness,Performance Bound and Beowulf Evaluation”, Neural Networks, vol. 20.1, pp. 109–128, 2007.
T
R. C. Watkins, Reynolds, K. M., DeMara, R., Georgiopoulos, M., Gonzalez, A., and Eaglin, R., “TRACKING DIRTY PROCEEDS: EXPLORING DATA MINING TECHNOLOGIES AS TOOLS TO INVESTIGATE MONEY LAUNDERING”, Police Practice and Research, vol. 4.2, pp. 163–178, 2003.
U
A. Gonzalez, Georgiopoulos, M., and DeMara, R., “Using Context-Based Neural Networks to Maintain Coherence Among Entities’ States in a Distributed Simulation”, The Journal of Defense Modeling and Simulation, vol. 4, pp. 147-172, 2007.
A. Gonzalez, Georgiopoulos, M., and DeMara, R., “Using context-based neural networks to maintain coherence in distributed simulations”, The Journal of Defense Modeling and Simulation, vol. 4, pp. 417-172, 2007.