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C
D. Charalampidis, Kasparis, T., Jones, W. L., and Georgiopoulos, M., “Use of multi-fractals to detect anomalous propagation (AP) in weather data”, in Proceedings SPIE; Conference on Signals and Data Processing of Small Targets, Orlando, FL, 2000.
D. Charalampidis, Kasparis, T., Georgiopoulos, M., and Rolland, J., “A Fuzzy ARTMAP based classification of natural textures”, Proceedings of the 18th International Conference of the North American Fuzzy Information Processing Society, pp. 507–511, 1999.
D. Charalampidis, Kasparis, T., and Georgiopoulos, M., “Texture classification using ART-based neural networks and Fractals”, SPIE Conference on Signal Processing,Sensor Fusion,and Target Recognition VII, pp. 212–222, 1998.
J. Castro, Georgiopoulos, M., and Secretan, J., “Analyzing the Fuzzy ARTMAP Matchtracking mechanism with Co-Objective Optimization Theory”, Proceedings of the International IEEE-INNS-ENNS Joint Conference on Neural Networks (IJCNN), pp. 743–748, 2007.
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.
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.
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, Secretan, J., Georgiopoulos, M., DeMara, R. F., Anagnostopoulos, G., and Gonzalez, A., “Pipelining of Fuzzy ARTMAP (FAM) without match-tracking”, in ANNIE 2004, St. Louis, MI, 2004.
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, 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.
K. Carr, Cannava, K., Pescatore, R., Georgiopoulos, M., and Anagnostopoulos, G., “Fast Stable and on-line training of Fuzzy ARTMAP using a novel, conservative, slow learning strategy”, in ANNIE 2004 conference, St. Louis, MI, 2004.
K. Carr, Cannava, K., Pescatore, R., Georgiopoulos, M., and Anagnostopoulos, G. C., “Fast Stable and on-line training of Fuzzy ARTMAP using a novel,conservative,slow learning strategy”, Neural Networks, pp. 63–69, 2004.
C.Li, M. Georgiopoulos, R. DeMara,, and G.C. Anagnostopoulos, M. Georgiopoulos, “Kernel Principal Subspace Mahalanobis Distances for Outlier Detection”, in International Joint Conference on Neural Networks (IJCNN), San Jose, CA, 2011.
B
J. R. Beck, Garcia, M. E., Zhong, M., Georgiopoulos, M., and Anagnostopoulos, G. C., “A Backward adjusting strategy for the C4.5 decision tree classifier, and the effect of the C4.5 parameters on the tree’s performance”, in 21st Artificial Intelligence Research Symposium, Coconut Grove, FL, 2008.
G. Bebis, Uthiram, S., and Georgiopoulos, M., “Face detection and verification using genetic search”, International Jornal on Artificial Intelligence Tools, vol. 9.2, pp. 225–245, 2000.
G. Bebis, Georgiopoulos, M., da Lobo, V. N., and Shah, M., “Learning affine transformations”, Pattern Recognition, vol. 32.10, pp. 1783–1799, 1999.
G. Bebis, Deaconu, T., and Georgiopoulos, M., “Fingerprint identification using Delaunay triangulation”, IEEE International Conference on Information,Intelligence and Systems, pp. 452–459, 1999.
G. Bebis, Uthiram, S., and Georgiopoulos, M., “Genetic Search for Face Detection and Verification”, IEEE International Conference on Information,Intelligence and Systems, pp. 360–367, 1999.
G. Bebis, Georgiopoulos, M., and da Lobo, V. N., “Using Self-Organizing Maps to Learn Geometric Hash Functions for Model-Based Object Recognition”, IEEE Transactions on Neural Networks, vol. 9.3, pp. 560–570, 1998.
G. Bebis, Georgiopoulos, M., Shah, M., and da Lobo, V. N., “Algebraic functions of views for indexing-based object recognition”, in Proceedings of the Sixth International Conference, India, 1998.
G. Bebis, Georgiopoulos, M., Shah, M., and da Lobo, V. N., “Using Algebraic Functions of Views for Indexing-Based Object Recognition”, International Conference on Computer Vision, pp. 634–639, 1998.
G. Bebis, Georgiopoulos, M., Shah, M., and da Lobo, V. N., “Indexing Based on Algebraic Functions of Views”, Computer Vision and Image Understanding, vol. 72.3, pp. 360–378, 1998.
G. Bebis, Georgiopoulos, M., and da Lobo, V. N., “Using self-organizing maps to learn geometric hash functions for model-based object recognition”, IEEE Transactions on Neural Networks, vol. 9, pp. 560-570, 1998.
G. Bebis, Georgiopoulos, M., and Bhatia, S., “Learning orthographic transformations for object recognition”, in 1997 IEEE International Conference on Systems, Man, and Cybernetics, Orlando, FL, 1997.
G. Bebis, Georgiopoulos, M., and Kasparis, T., “Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization”, Neurocomputing, pp. 167–194, 1997.
G. Bebis, Georgiopoulos, M., and Kasparis, T., “Coupling weight elimination and genetic algorithms”, in International Conference on Neural Networks (ICNN), Washington, DC, 1996.
G. Bebis, Georgiopoulos, M., da Lobo, V. N., and Shah, M., “Learning affine transformations of the plane for model-based object recognition”, in 13th International Conference on Pattern Recognition (ICPR-96), Vienna, Austria, 1996.
G. Bebis and Georgiopoulos, M., “Improving generalization by using genetic algorithms to determine the neural network size”, in Southcon 95, 1995.
G. Bebis, Georgiopoulos, M., and da Lobo, V. N., “Learning geometric hashing functions for model-based object recognition”, in Fifth International Conference on Computer Vision, ICCV-1995, Cambridge, MA, 1995.
G. Bebis and Georgiopoulos, M., “Optimal feed-forward neural network architectures”, IEEE Potentials, pp. 27-31, 1994.
G. Bebis, Papadourakis, G. M., Georgiopoulos, M., and Heileman, G. L., “Increasing classification accuracy using multiple-neural-network schemes”, in Proceedings of the 1992 SPIE Conference (Technical Program on Intelligent Information Systems), Orlando, FL, 1992.
G. Bebis, Papadourakis, G. M., and Georgiopoulos, M., “Back-Propagation: Increasing rate of convergence by predictable pattern loading”, vol. 1, pp. 14-30, 1989.
M. Bassiouni, Georgiopoulos, M., and Chiu, M., “Performance of standard and modified network protocols in a real-time application”, in 1997 International Performance, Computing and Communications Conference, Scottsdale, Arizona, 1997.
M. Bassiouni, Georgiopoulos, M., and Thompson, J., “Analytical and simulation models for real time networks”, in Proceedings of the 21st Annual Pittsburgh Conference, University of Pittsburgh, School of Engineering, Pittsburgh, Pasadena, 1990.
M. Bassiouni, Georgiopoulos, M., and Thompson, J., “Real time simulation networking Network modeling and protocol alternatives”, in 11th Interservice / Industry Training Systems Conference, Fort Worth, Texas, 1989.
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.
A
G. C. Anagnostopoulos, Georgiopoulos, M., Ports, K., Richie, S., Cardinale, N., White, M., Kepuska, V., Chan, P. K., Wu, A., and Kysilka, M., “Project EMD-MLR: Educational Materials Development and Research in Machine Learning for Undergraduate students”, in 2006 ASEE, Chicago, Illinois, 2006.
G. C. Anagnostopoulos, Georgiopoulos, M., Ports, K., Richie, S., Cardinale, N., White, M., Kepuska, V., Chan, P. K., Wu, A., and Kysilka, M., “Project EMD-MLR: Educational Materials Development and Research in Machine Learning for Undergraduate students”, in the ASEE 2005 Annual Conference and Exposition, Portland, Oregon, 2005.
G. C. Anagnostopoulos and Georgiopoulos, M., “Putting the utility of match tracking in Fuzzy ARTMAP training to the test”, pp. 1–6, 2003.
G. C. Anagnostopoulos, Bharadwaj, M., Georgiopoulos, M., Verzi, S., and Heileman, G., “Exemplar-based pattern recognition via semi-supervised learning”, Neural Networks, pp. 2782–2787, 2003.
G. C. Anagnostopoulos, Georgiopoulos, M., Verzi, S., and Heileman, G., “Reducing generalization error and category proliferation in ellipsoid ARTMAP via tunable misclassification error tolerance: Boosted Ellipsoid ARTMAP”, Neural Networks, pp. 2650–2655, 2002.
G. C. Anagnostopoulos and Georgiopoulos, M., “Category regions as new geometrical concepts in Fuzzy-ART and Fuzzy-ARTMAP”, Neural Networks, vol. 15.10, pp. 1205-1221, 2002.
G. C. Anagnostopoulos and Georgiopoulos, M., “Ellipsoid ART/ARTMAP category regions for the choice-by-difference functions”, in Conference on Applications and Science of Computational Intelligence V, Orlando, FL, 2002.
G. C. Anagnostopoulos, Georgiopoulos, M., Verzi, S., and Heileman, G., “Boosted ellipsoid ARTMAP”, Proceedings of SPIE,Conference on Applications and Science of Computational Intelligence, vol. 4739, pp. 74–85, 2002.
G. C. Anagnostopoulos and Georgiopoulos, M., “Ellipsoid ART/ARTMAP category regions for the choice-by-difference functions”, Proceedings of SPIE,Conference on Applications and Science of Computational Intelligence V, vol. 4739, pp. 62–73, 2002.
G. C. Anagnostopoulos and Georgiopoulos, M., “New Geometrical Concepts in Fuzzy-ART and Fuzzy-ARTMAP: Category Regions”, IEEE-INNS International Joint Conference on Neural Networks (IJCNN 2001), pp. 32–37, 2001.
G. C. Anagnostopoulos and Georgiopoulos, M., “Ellipsoid ART and ARTMAP for incremental clustering and classification”, Proceedings of the International Joint Conference on Neural Networks, pp. 1221–1226, 2001.
G. Anagnostopoulos and Georgiopoulos, M., “Ellipsoidal ART and ARTMAP for incremental unsupervised and supervised learning”, in Conference on Applications and Science of Computational Intelligence IV, Orlando, FL, 2001.
G. C. Anagnostopoulos and Georgiopoulos, M., “Ellipsoid ART and ARTMAP for incremental unsupervised and supervised Learning”, Proceedings of SPIE; Conference on Applications and Science of Computational Intelligence IV, pp. 293–304, 2001.
G. C. Anagnostopoulos and Georgiopoulos, M., “New geometrical perspective of fuzzy ART and fuzzy ARTMAP learning”, Proceedings of SPIE; Conference on Applications and Science of Computational Intelligence IV, pp. 22–32, 2001.
G. C. Anagnostopoulos and Georgiopoulos, M., “Hypershere ART and ARTMAP for unsupervised and supervised incremental learning”, in the International Joint Conference on Neural Networks, Como, Italy, 2000.
G. C. Anagnostopoulos and Georgiopoulos, M., “Hypersphere ART and ARTMAP for Unsupervised and Supervised,Incremental Learning”, Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 59–64, 2000.
G. C. Anagnostopoulos, Georgiopoulos, M., Nickerson, D., and Bebis, G., “Ensembles of hybrid intelligent experts”, in 1997 IEEE International Conference on Systems, Man, and Cybernetics, Orlando, FL, 1997.
A. Al-Daraiseh, Kaylani, A., Georgiopoulos, M., Mollaghasemi, M., Anagnostopoulos, G. C., and Kaburlasos, V. G., “Genetically Engineered ART Architectures”, Heidelberg: Springer-Verlag,in Computational Intelligence Based on Lattice Theory,Studies in Computational Intelligence, vol. 67, pp. 233–262, 2007.
A. Al-Daraiseh, Kaylani, A., Georgiopoulos, M., Wu, A. S., Mollaghasemi, M., and Anagnostopoulos, G. C., “GFAM:Evolving Fuzzy ARTMAP Neural Networks”, vol. 20.8, pp. 874–892, 2007.
A. Al-Dairaseh, Georgiopoulos, M., Wu, A. S., Anagnostopoulos, G., and Mollaghasemi, M., “GFAM: A genetic algorithm optimization of Fuzzy ARTMAP”, in WCCI 2006 (Fuzzy Systems), Vancouver, Canada, 2006.
A. Al-Dairaiseh, Kaylani, A., Georgiopoulos, M., Mollaghasemi, M., Wu, A. S., and Anagnostopoulos, G., “GFAM Evolving Fuzzy ARTMAP Neural Networks”, Neural Networks journal, vol. 20, pp. 874-892, 2007.

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