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G
M. Georgiopoulos, Correction to ‘Packet error probabilities in frequency hopped spread-spectrum packet radio networks-memoryless frequency hopping patterns considered’, IEEE Trans. on Comm., vol. 39, pp. 362-364, 1991.
M. Georgiopoulos, Heileman, G. L., and Huang, J., Properties of learning in ART1, Neural Networks, vol. 4, pp. 751-758, 1991.
M. Georgiopoulos, Heileman, G. L., and Huang, J., Convergence properties of learning in ART1, Neural Computation, vol. 2, pp. 502-510, 1990.
M. Georgiopoulos, On the error probability of coded frequency hopped spread spectrum multiple access systems with more than one code symbols per dwell interval, IEEE Trans. on Comm., vol. 38, pp. 1321-1324, 1990.
M. Georgiopoulos, Packet error probabilities in direct sequence spread spectrum packet radio networks, IEEE Trans. on Comm., vol. 38, pp. 1599-1606, 1990.
M. Georgiopoulos, On the error probability of coded frequency-hopped spread-spectrum multiple-access systems with more than one code symbol per dwell interval Conference, in MILCOM 89, Boston, Massachusetts, 1989.
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.
M. Georgiopoulos, Packet error probabilities in direct sequence spread spectrum packet radio networks with BCH codes, in MILCOM 88, San Diego, California, 1988.
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.
M. Georgiopoulos, Packet error probabilities in frequency hopped spread spectrum packet radio networks–Memoryless frequency hopping patterns considered, IEEE Trans. on Comm., vol. 36, pp. 720-724, 1988.
M. Georgiopoulos, Packet error probabilities in frequency hopped spread spectrum packet radio networks–Memoryless frequency hopping patterns considered, in the 26th IEEE Conference on Decision and Control, Los Angeles, California, 1987.
M. Georgiopoulos and Papantoni-Kazakos, P., Collision resolution algorithms utilizing absorptions and collision multiplicities, IEEE Trans. on Comm., vol. 33, pp. pp. 401-404, 1985.
M. Georgiopoulos and Papantoni-Kazakos, P., Collision resolution protocols utilizing absorptions and collision multiplicities, in Abstracts of the 1983 International Symposium on Information Theory, St. Jovite, Canada, 1983.
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.
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. J. Gonzalez, Gerber, W. J., DeMara, R. F., and Georgiopoulos, M., Context-driven Near-term Intention Recognition, Journal of Defense Modeling and Simulation, vol. 1.3, pp. 153–170, 2004.
A. J. Gonzalez, DeMara, R. F., and Georgiopoulos, M., Vehicle model generation and optimization for embedded simulation, Simulation Interoperability Workshop, pp. 206–213, 1998.
A. J. Gonzaleza, Georgiopoulos, M., DeMara, R. F., Henninger, A., and Gerber, W., Automating the CGF Model Development and Refinement Process by Observing Expert Behavior in a Simulation, 7th Conference on Computer Generated Forces and Behavioral Representation, 1998.
M. Gul, Catbas, F. N., and Georgiopoulos, M., Application of Pattern Recognition Techniques to Identify Structural Change in a Laboratory Specimen, in he SPIE Smart Structures and Materials & Nondestructive Evaluation and Health Monitoring Conference, San Diego, CA, 2007.
H
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.
G. L. Heileman, Georgiopoulos, M., and Abdallah, C., A dynamical adaptive resonance architecture, IEEE Transactions on Neural Networks, vol. 5, pp. 873-889, 1994.
G. L. Heileman, Georgiopoulos, M., and Roome, W. D., A general framework for concurrent simulation on neural network models, in International Joint Conference on Neural Networks (IJCNN), Baltimore, MD, 1992.
G. L. Heileman, Georgiopoulos, M., and Roome, W. D., A general framework for concurrent simulation on neural network models, IEEE Transactions on Software Engineering, vol. 18, pp. 551-562, 1992.
G. L. Heileman and Georgiopoulos, M., The augmented ART1 neural network, in Proceedings of the International Joint Conference on Neural Networks (IJCNN), Seattle, Washington, 1991.
G. L. Heileman, Papadourakis, G. M., and Georgiopoulos, M., A neural network associative memory for real-time applications, Neural Computation, vol. 2, pp. 107-115, 1990.
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.
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.
A. Henninger, Gonzalez, A., Georgiopoulos, M., and DeMara, R., Modeling Semi-Automated Forces with Neural Networks: Performance Improvement through a Modular Approach, 2000.
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.
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. S. Ho, Liou, J. J., and Georgiopoulos, M., Hardware implementation of ART1 memories using a mixed analogdigital approach, in IEEE World Congress on Computational Intelligence, Orlando, Fl, 1994.
C. S. Ho, Liou, J. J., Georgiopoulos, M., and Christodoulou, C., Hardware implementation of an adaptive resonance theory (ART) neural network using compensated amplifiers, in Proceedings SPIE, Orlando, FL, 1994.
C. S. Ho, Liou, J. J., Georgiopoulos, M., Heileman, G. L., and Christodoulou, C., Analog circuit design and implementation of an adaptive resonance theory (ART) neural network architecture, International Journal of Electronics, vol. 76, pp. 271-291, 1994.
C. S. Ho, Liou, J. J., and Georgiopoulos, M., Design and simulation of analog circuits for adaptive resonance theory (ART) neural networks, in Symposium on Semiconductor Theory and Simulation, Taipei, Taiwan, 1993.
J. W. House, Abdallah, C., Heileman, G. L., and Georgiopoulos, M., An application of gradient-like dynamics to neural networks, in Southcon 1994, Orlando, FL, 1994.
J. Huang, Georgiopoulos, M., and Heileman, G. L., Fuzzy ART properties, Neural Networks, vol. 8, pp. 203-213, 1995.
J. Huang, Georgiopoulos, M., and Heileman, G. L., Properties of learning in fuzzy ART, in IEEE World Congress on Computational Intelligence, Orlando, Fl, 1994.
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.
K
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.
T. Kasparis, Georgiopoulos, M., and Payne, E., Non-linear filtering techniques for narrow-band interference rejection in direct sequence spread-spectrum systems, in MILCOM 91, McLean, Virginia, 1991.
A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G., MO-GART: An adaptive multi-objective approach to evolving ART architectures, IEEE Transactions on Neural Networks, vol. 21, pp. 529-550, 2010.
A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G., MO-GART: An adaptive multi-objective approach to evolving ART architectures, IEEE Transactions on Neural Networks, vol. 21, pp. 529-550, 2010.
A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G., AG-ART: An Adaptive Method of Evolving ART Architectures, Neurocomputing, vol. 72, pp. 2079-2092, 2009.
A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G., Efficient Evolution of ART Neural Networks, in 2008 IEEE Congress on Evolutionary Computation (IEEE CEC 2008), 2008.
A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G., MO-GART: Multi-Objective Optimization of ART Architectures, in 2008 IEEE Congress on Evolutionary Computation (IEEE CEC 2008), Hong Kong, 2008.
A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G. C., M-GFAM:An Elegant Approach to Genetically Optimize Fuzzy ARTMAP Neural Network Architectures, Proceedings of the 8th International Conference on Natural Computing (ICNC 2007),part of the 10th Joint Conference on Information Sciences (JCIS 2007), pp. 1617-1623, 2007.
A. Kaylani, Al-Daraiseh, A., Georgiopoulos, M., Mollaghasemi, M., Anagnostopoulos, G. C., and Wu, A. S., Genetic Optimization of ART Neural Network Architectures, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2007), pp. 379–384, 2007.
J. Klodzinski, Al-Daraiseh, A., Georgiopoulos, M., and Al-Deek, H. M., Development of a java applet for generating truck trips from freight data, Transportation Research Record No. 1870, pp. 10–17, 2004.
A. Koufakou and Georgiopoulos, M., A fast outlier detection strategy for distributed high dimensional datasets with mixed attributes, Data Mining and Knowledge Discovery, vol. 20, pp. 259-289, 2010.
A. Koufakou, Secretan, J., and Georgiopoulos, M., Non-derivable itemsets for fast outlier detection in large high dimensional categorical data, Knowledge and Information Systems, pp. 1-29, 2010.
A. Koufakou, Georgiopoulos, M., and Anagnostopoulos, G. C., Detecting Outliers in High-Dimensional Datasets with Mixed Attributes, International Conference on Data Mining (DMIN 2008), 2008.
A. Koufakou, Secretan, J., Reeder, J., Cardona, K., and Georgiopoulos, M., Fast Parallel Outlier Detection for Categorical Datasets using MapReduce, IEEE World Congress on Computational Intelligence (WCCI), 2008.
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.
A. Koufakou, Weihs, N., Georgiopoulos, M., and Al-Daraiseh, A., Comparisons of Gaussian ARTMAP and Distributed Gaussian ARTMAP: The Category Proliferation problem, in ANNIE 2006, St. Louis, Missouri, 2006.
A. Koufakou, Weihs, N., Georgiopoulos, M., and Al-Daraiseh, A., Comparisons of Gaussian ARTMAP and Distributed Gaussian ARTMAP Classifiers - The Category Proliferation Problem, Evolutionary Programming,Complex Systems and Artificial Life also presented at the 2006 Artificial Neural Networks in Engineering (ANNIE), vol. 16, pp. 695–704, 2006.
A. Koufakou, Wakchaure, A., Kursun, O., Georgiopoulos, M., Reynolds, K., and Eaglin, R., Burglary data mining – A three tiered approach: Local satte and nation-wide, in the 2nd Annual GIS Symposium at TU, Troy, Alabama, 2005.
A. Koufakou, Georgiopoulos, M., Anagnostopoulos, G. C., and Kasparis, T., Cross-validation in Fuzzy ARTMAP for large databases, Neural Networks, vol. 14.9, pp. 1279-1291, 2001.
O. Kursun, Koufakou, A., Wakchaure, A., Georgiopoulos, M., Reynolds, K., and Eaglin, R., Answer: approximate Name Search with Errors in Large Databases by a Novel Approach Based on Prefix-dictionary, International Journal on Artificial Intelligence Tools, vol. 15.5, pp. 839–848, 2006.
O. Kursun, Koufakou, A., Chen, B., Georgiopoulos, M., Reynolds, K., and Eaglin, R., A Dictionary-Based Approach to Fast and Accurate Name Matching in Large Law Enforcement Databases, IEEE Intelligence and Security Informatics (ISI) Conference, pp. 72–82, 2006.
L
C. Li, Georgiopoulos, M., and Anagnostopoulos, G. C., Kernel-based Distance Metric Learning in the Output Space, in International Joint Conference on Neural Networks (IJCNN), Dallas, TX, August 04-09, 2013.
C. Li, Georgiopoulos, M., and Anagnostopoulos, G. C., Conic Multi-Task Classification, in ECML PKDD 2014, Nancy, France, September 15-19, 2014.
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.
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, Georgiopoulos, M., and Anagnostopoulos, G. C., Multi-Task Classification Hypothesis Space with Improved Generalization Bounds , IEEE Transactions on Neural Networks and Learning Systems, vol. accepted for publication (acceptance was communicated in July 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.
C. Li, M. Georgiopoulos, R. DeMara,, and Anagnostopoulos, G. C., Multi-Task Classification Hypothesis Space with Improved Generalization Bounds, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 7, pp. 1468-1479, 2015.
M
L. Massi, Lancey, P., Nair, U., Straney, R., Georgiopoulos, M., and Young, C., Engineering and Computer Science Community College Transfers and Native Freshmen Students: Relationships Among Participation in Extra-Curricular and Co-Curricular Activities, Connecting to the University Campus, and Academic Success, in Frontiers in Education Conference, Seattle, WA, October 3-6, 2012.
L. Massi, Georgiopoulos, M., Young, C. Y., Ford, C. M., Lancey, P., Bhati, D., and Small, K. A., Internships and Undergraduate Research: Impact, Support, and Institutionalization of an NSF S-STEM Program through Partnerships with Industry and Funding from Federal and Local Workforce Agencies, Proceedings of the 120th ASEE Conference and Exposition. Atlanta, GA, June 23-26, 2013.
L. Merakos and Georgiopoulos, M., Analysis of a multi-hop CDMA packet radio network, in the 26th IEEE Conference on Decision and Control, Los Angeles, California, 1987.
A. Meyer-Base, Jancke, K., Wissmuller, A., and Georgiopoulos, M., Fast K-dimensional tree-structured vector quantization encoding method for image compression, Optical Engineering Letters, vol. 43, pp. 1012-1013, 2004.
R. Miguez, Georgiopoulos, M., and Kaylani, A., A Genetically Engineered Probabilistic Neural Network, Nonlinear Analysis: Theory, Methods & Applications, vol. 73, pp. 1783-1791, 2010.
F. Miwakeichi, Ramirez-Padron, R., and P. Valdes-Sosa, O. T., A comparison of Non-linear Non-parametric Models for Epilepsy Data, Computers in Biology and Medicine, vol. 31.1, pp. 41–57, 2001.
M. Mollaghasemi, Georgiopoulos, M., Cope, D., Donnelly, A., and Steele, M., Educating Middle and High School Students in Space Operations, in the 2004 Winter Simulation Conference, Washington, DC, 2004.
M. Mollaghasemi, LeCroy, K., and Georgiopoulos, M., Application of neural networks and simulation modeling in manufacturing system design, INTERFACES, vol. 28, pp. 100-114, 1998.
M. Mollaghasemi, LeCroy, K., and Georgiopoulos, M., Applications of neural networks and simulation modeling in manufacturing system design, in Southcon 96, 1996.
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G. Rabadi, Anagnostopoulos, G. C., and Mollaghasemi, M., A heuristic algorithm for the just-in-time single machine scheduling problem with setups: A comparison with simulated annealing, vol. 32, pp. 326–335, 2007.
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.
J. Reeder, Gita, S., Georgiopoulos, M., and Anagnostopoulos, G. C., Intelligent Trading Agents for Massively Multi-player Game Economies, Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference, pp. 102–107, 2008.
J. Reeder, Miguez, R., Sparks, J., Georgiopoulos, M., and Anagnostopoulos, G. C., Interactively Evolved Modular Neural Networks for Game Agent Control, IEEE Symposium on Computational Intelligence and Games (CIG 08, pp. 167–174, 2008.
J. Reeder, Georgiopoulos, M., Castro, J., Burns, S., Anagnostopoulos, G., and Mollaghasemi, M., Hilbert Space Filling Curve Nearest Neighbor, in The ISAS and CITSA 2005, Orlando, FL, 2005.
J. Reeder and Georgiopoulos, M., Generative Neural Networks for Multi-Task Life-Long Learning, The Computer Journal, vol. 57, no. 3, pp. 427-450, 2014.
K. Reynolds, Wakchaure, A., Kursun, O., Georgiopoulos, M., Reynolds, K., and Eaglin, R., Burglary data mining – A three tiered approach: Local state and nation-wide, in the 2nd Annual GIS Symposium at TU, Troy, Alabama, 2005.
K. Reynolds, Kursun, O., Eaglin, R., Chen, B., and Georgiopoulos, M., Development of an Artificial Intelligent System for detection and visualization of auto theft recovery patterns, in Computational Intelligence Conference on Homeland Security and Public Safety (CIHSPS, 2005), Orlando, FL, 2005.
K. Reynolds, Kursun, O., Georgiopoulos, M., and Eaglin, R., Development of an Artificial Intelligence Clustering Algorithm to Detect Auto Theft Recovery Patterns, in GIS Symposium 2004, Troy, AL, 2004.
X. Rui, Anagnostopoulos, G. C., and II, W. D., Multiclass Cancer Classification Using Semisupervised Ellipsoid ARTMAP and Particle Swarm Optimization with Gene Expression Data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4., pp. 65–77, 2007.
I. Russel, Georgiopoulos, M., Castro, J., Neller, T., McCracken, D., and Bouvier, D., Condensing the CC-2001 core in an Integrated Curriculum, in CCSCNE (Consortium for Computing in Small Colleges in the Northeast), Providence, RI, 2003.

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