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G. M. Papadourakis, Heileman, G. L., and Georgiopoulos, M., “A parallel implementation of the Hopfield network on GAPP processors”, in Proceedings of the International Joint Conference on Neural Networks, Washington, DC, 1989.

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. Secretan, Georgiopoulos, M., Anagnostopoulos, G. C., Tapia, J., Chadha, A., and Huber, B., “Parallelizing the Fuzzy ARTMAP Algorithm on a Beowulf Clusters”, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2005), pp. 475–480, 2005.

T. Zhang, M. Georgiopoulos, R. DeMara,, and Anagnostopoulos, G. C., “Pareto Optimal Selection via SPRINT-Race”, IEEE Transactions on Cybernetics, vol. 48, no. 2, pp. 596-610, 2018.

C. Li, Georgiopoulos, M., and Anagnostopoulos, G. C., “Pareto-Path Multi-task Multiple Kernel Learning”, IEEE Transactions on Neural Networks and Learning Systems, vol. accepted for publication (acceptance was communicated in June 2014), 2014.

C. Li, M. Georgiopoulos, R. DeMara,, and Anagnostopoulos, G. C., “Pareto-Path Multi-Task Multiple Kernel Learning”, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 1, pp. 51-61, 2015.

J. Castro, Georgiopoulos, M., DeMara, R., and Gonzalez, A., “A Partitioned Fuzzy ARTMAP implementation for fast processing of large databases on sequential machines”, 2004.

G. G. M. M. A. J. H. J.J. Vargas, R.F. DeMara, “PDU Bundling and Replication for Reduction of Distributed Simulation Communication Traffic”, Methodology,Technology,Journal of Defense Modeling and Simulation: Applications, vol. 1.3, pp. 171–183, 2004.

J. J. Vargas, DeMara, R. F., Gonzalez, A. J., Georgiopoulos, M., and Marshall, H., “PDU Bundling and Replication for Reduction of Distributed Simulation Communication Traffic”, Journal of Defense Modeling and Simulation, vol. 1.3, pp. 171–183, 2004.

M. Georgiopoulos, “Performance evaluation of frequency hopped receiver oriented spread spectrum packet radio networks”, in Proceedings of the International Conference on Communications, Boston, Massachusetts, 1989.

H. T. Owens, Georgiopoulos, M., and Belkerdid, M., “Performance of BCH and convolutional codes in direct sequence spread spectrum packet radio networks”, in MILCOM 89, Boston, Massachusetts, 1989.

E. A. H. Zooghby, Christodoulou, C. G., and Georgiopoulos, M., “Performance of Radial-Basis Function Networks for Direction of Arrival Estimation with Antenna Arrays”, IEEE Transactions on Antennas and Propagation, vol. 45.11, pp. 1611–1617, 1997.

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.

J. Secretan, Castro, J., Chadha, A., Huber, B., Tapia, J., Georgiopoulos, M., Anagnostopoulos, G., and Richie, S., “Pipelining of ART architectures (FAM, EAM, GAM) without match-tracking (MT)”, in 2005 Artificial Neural Networks in Engineering, St. Louis, MI, 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, 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. Secretan, Georgiopoulos, M., and Castro, J., “A Privacy Preserving Probabilistic Neural Network for Horizontally Partitioned Databases”, 2007.

M. Zhong, Hecker, J., Maidhoff, I., Shibly, P., Georgiopoulos, M., Anagnostopoulos, G., and Mollaghasemi, M., “Probabilistic Neural Network: Comparisons of the Cross-Validation Approach and a Fast Heuristic to choose the Smoothing Parameters”, in 2005 Artificial Neural Networks in Engineering, St. Louis, MI, 2005.

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.

M. Georgiopoulos, Heileman, G. L., and Huang, J., “Properties of learning in ART1”, in Proceedings of the International Joint Conference on Neural Networks, Singapore, 1991.

M. Georgiopoulos, Heileman, G. L., and Huang, J., “Properties of learning in ART1”, Neural Networks, vol. 4, pp. 751-758, 1991.

J. Huang, Georgiopoulos, M., and Heileman, G. L., “Properties of learning in fuzzy ART”, in IEEE World Congress on Computational Intelligence, Orlando, Fl, 1994.

M. Georgiopoulos, Dagher, I., Heileman, G. L., and Bebis, G., “Properties of Learning of a Fuzzy ART Variant”, Neural Networks, vol. 12.6, pp. 837–850, 1999.

G. C. Anagnostopoulos and Georgiopoulos, M., “Putting the utility of match tracking in Fuzzy ARTMAP training to the test”, pp. 1–6, 2003.

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S. J. Verzi, Heileman, G. L., Georgiopoulos, M., and Healy, M. J., “Rademacher penalization applied to Fuzzy ARTMAP and Boosted ARTMAP”, Proceedings of the International Joint Conference on Neural Networks, pp. 1191–1196, 2001.

E. A. H. Zooghby, Christodoulou, C. G., and Georgiopoulos, M., “Radial basis function neural network algorithm for adaptivebeamforming in cellular communication systems”, IEEE-APS Conference on Antennas and Propagation for Wireless Communications, pp. 53–56, 1998.

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.

E. Nold, Tucker, K., Long, R., and Georgiopoulos, M., “Real-time unsupervised neural networks for non-implementable in natural noise A refutable hypothesis based on experiment”, in Proceedings of the International Joint Conference on Neural Networks (IJCNN), Seattle, Washington, 1991.

Y. Huang, Li, C., Georgiopoulos, M., and Anagnostopoulos, G. C., “Reduced Rank Local Distance Metric Learning”, in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML and PKDD 2013), Prague, Czech Republic, September 23-27, 2013.

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.

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A. Koufakou, Ortiz, E., Georgiopoulos, M., Anagnostopoulos, G. C., and Reynolds, M. K., “A scalable and efficient otlier detection strategy for categorical data”, in Annual IEE International Conference on Tools with Artificial Intelligence, 2007 (ICTAI 2007),, Patras, Greece, 2007.

A. Koufakou, Ortiz, E., Georgiopoulos, M., Anagnostopoulos, G. C., and Reynolds, K., “A Scalable and Efficient Outlier Detection Strategy for Categorical Data”, International Conference on Tools with Artificial Intelligence (ICTAI), 2007.

W. J. Park, Jones, L., Charalampidis, D., Kasparis, T., and Georgiopoulos, M., “Sea-Ice extent classification using active/passive microwave measurements from QuickScat”, in AGU Spring meeting, Washington DC, 2001.

T. Kasparis, Charalampidis, D., Georgiopoulos, M., and Rolland, J. P., “Segmentation of textured images based on fractals and image filtering”, Pattern Recognition, vol. 34.10, pp. 1963–1973, 2001.

T. Kasparis, Charalampidis, D., Georgiopoulos, M., and Rolland, J. P., “Segmentation of textured images based on fractals and image filtering”, Pattern Recognition, vol. 34.10, pp. 1963–1978, 2001.

M. Georgiopoulos, Skarman, S., and Gonzalez, A. J., “Short-term electric load forecasting using a Fuzzy ARTMAP neural network”, in Conference on Applications and Science of Computational Intelligence, Orlando, FL, 1998.

S. E. Skarman, Georgiopoulos, M., and Gonzalez, A. J., “Short-term electrical load forecasting using a fuzzy ARTMAP neural network”, Conference on Applications and Science of Computational Intelligence, pp. 181-191, 1998.

R. Ramirez-Padron, Foregger, D., Manuel, J., Georgiopoulos, M., and Mederos, B., “Similarity Kernels for Nearest Neighbor-based Outlier Detection”, Lecture Notes in Computer Science, vol. 6065/2010, pp. 159-170, 2010.

C. Sentelle, Anagnostopoulos, G. C., and M. Georgiopoulos, R. DeMara, “A Simple Method for Solving the SVM Regularization Path for Semi-Definite Kernels”, IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 4, pp. 709-722, 2016.

M. Georgiopoulos and Spillers, R. M., “A simulation study of a limited sensing random access algorithm for a local area network with voice users”, in Proceedings Southeastcon, Knoxville, Tennessee, 1988.

C. Christodoulou and Georgiopoulos, M., “Smart adaptive array antennas for wireless communcations”, Proceedings of SPIE; Conference on Digital Wireless Communication III, pp. 75–83, 2001.

T. Zhang, Georgiopoulos, M., and Anagnostopoulos, G. C., “S-RACE: A Multi-objective Racing Algorithm”, GECCO 2013, Proceedings of the fifteenth annual conference on Genetic and evolutionary computation conference. Amsterdam, the Netherlands, July 6-10, pp. 1565-1572, 2013.

G. L. Heileman, Georgiopoulos, M., and Huang, J., “A survey of learning results for ART1 networks”, in IEEE World Congress on Computational Intelligence, Orlando, FL, 1994.

M. Georgiopoulos, DeMara, R. F., Gonzalez, A. J., Wu, A. S., Mollaghasemi, M., Gelenbe, E., Kysilka, M., Secretan, J., Sharma, C. A., and Alnsour, A. J., “A Sustainable Model for Integrating Current Topics in Machine Learning Research in the Undergraduate Curriculum”, IEEE Transactions on Education, vol. 52, pp. 503-512, 2009.

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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.

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.

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C. Li, Georgiopoulos, M., and Anagnostopoulos, G. C., “A Unifying Framework for Typical Multitask, Multiple Kernel Learning Problems”, IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 7, pp. 1287-1297, 2014.

S. Verzi, Heileman, G., Georgiopoulos, M., and Anagnostopoulos, G. C., “Universal approximation with Fuzzy ART and Fuzzy ARTMAP”, Neural Networks, pp. 1987–1992, 2003.

D. Charalampidis, Kasparis, T., Jones, L., and Georgiopoulos, M., “Use of multifractals to detect anomalous propagation (AP) in weather radar”, Proceedings SPIE; Conference on Signals and Data Processing of Small Targets, pp. 13–22, 2008.

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.

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.

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.

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., 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.

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A. J. Gonzalez, DeMara, R. F., and Georgiopoulos, M., “Vehicle model generation and optimization for embedded simulation”, Simulation Interoperability Workshop, pp. 206–213, 1998.

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T. Zhang, Borrero, G., and Georgiopoulos, M., “A Winner-Take-All Methodology: Finding the Best Evolutionary Algorithm for the Global Optimization of Functions”, Genetic and Evolutionary Computation Conference. Philadelphia, July 7th-July 11th, pp. 1515-1516, 2012.

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