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Conference Paper

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.

C. Christodoulou, Huang, J., Georgiopoulos, M., and Liou, J. J., “Application of the ARTMAP neural network in the design of cascaded gratings and frequency selective surfaces”, in International IEEE Antennas and Propagation Symposium, Seattle, 1994.

M. Mollaghasemi, LeCroy, K., and Georgiopoulos, M., “Applications of neural networks and simulation modeling in manufacturing system design”, in Southcon 96, 1996.

C. Li, Georgiopoulos, M., and Anagnostopoulos, G. C., “Conic Multi-Task Classification”, in ECML PKDD 2014, Nancy, France, September 15-19, 2014.

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.

C. Christodoulou, Huang, J., Georgiopoulos, M., and Liou, J. J., “Design of gratings and frequency selective surfaces using Fuzzy ARTMAP neural networks”, in Proceedings SPIE, Orlando, FL, 1994.

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.

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., and Georgiopoulos, M., “Hardware implementation of ART1 memories using a mixed analogdigital approach”, in IEEE World Congress on Computational Intelligence, Orlando, Fl, 1994.

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.

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, 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.

M. Georgiopoulos, Li, C., and Kocak, T., “Learning in the Feed-Forward Random Neural Network: A Critical Review”, in the 25th International Symposium on Computer and Information Sciences, London, UK, 2010.

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.

Conference Proceedings

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.

Journal Article

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.

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.

J. Secretan, Lawson, M., and Boloni, L., “Brokering Algorithms for Composing Low Cost Distributed Storage Resources”, 2007.

W. D. Jr., Liou, J. J., and Georgiopoulos, M., “Circuit simulation of adaptive resonance (ART) neural networks using PSpice”, International Journal of Electronics, vol. 74, pp. 101-110, 1993.

C. Young, Georgiopoulos, M., Hagen, S., Geiger, C., Dagley-Falls, M., Islas, A., Ramsey, P., Lancey, P., Forde, D., and Bradbury, E., “Improving Student Learning in Calculus through Applications”, International Journal of Mathematical Education in Science and Technology, vol. 42, pp. 591-604, 2011.

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., da Lobo, V. N., and Shah, M., “Learning affine transformations”, Pattern Recognition, vol. 32.10, pp. 1783–1799, 1999.

M. Georgiopoulos, Li, C., and Kocak, T., “Learning in the Feed-Forward Random Neural Network: A Critical Review”, Performance Evaluation, 2010.

M. Georgiopoulos, Li, C., and Kocak, T., “Learning in the Feed-Forward Random Neural Network: A Critical Review”, Performance Evaluation, vol. 68, pp. 361-384, 2011.

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., “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.

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.

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.

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., 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 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.