Publications

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Beck, JR, Garcia ME, Zhong M, Georgiopoulos M, Anagnostopoulos GC.  2008.  A Backward adjusting strategy for the C4.5 decision tree classifier, and the effect of the C4.5 parameters on the tree’s performance. 21st Artificial Intelligence Research Symposium.
Bebis, G, Georgiopoulos M, da Lobo VN.  1995.  Learning geometric hashing functions for model-based object recognition. Fifth International Conference on Computer Vision, ICCV-1995.
Bebis, G, Georgiopoulos M, Shah M, da Lobo VN.  1998.  Using Algebraic Functions of Views for Indexing-Based Object Recognition. International Conference on Computer Vision. :634–639.
Bebis, G, Georgiopoulos M, Kasparis T.  1996.  Coupling weight elimination and genetic algorithms. International Conference on Neural Networks (ICNN).
Bebis, G, Uthiram S, Georgiopoulos M.  1999.  Genetic Search for Face Detection and Verification. IEEE International Conference on Information,Intelligence and Systems. :360–367.
Bebis, G, Georgiopoulos M, Kasparis T.  1997.  Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization. Neurocomputing. :167–194.
Bebis, G, Uthiram S, Georgiopoulos M.  2000.  Face detection and verification using genetic search. International Jornal on Artificial Intelligence Tools. 9.2:225–245.
Bebis, G, Georgiopoulos M.  1994.  Optimal feed-forward neural network architectures. IEEE Potentials. :27-31.
Bebis, G, Georgiopoulos M, Shah M, da Lobo VN.  1998.  Indexing Based on Algebraic Functions of Views. Computer Vision and Image Understanding. 72.3:360–378.
Bebis, G, Georgiopoulos M, da Lobo VN, Shah M.  1996.  Learning affine transformations of the plane for model-based object recognition. 13th International Conference on Pattern Recognition (ICPR-96).
Bebis, G, Georgiopoulos M, da Lobo VN.  1998.  Using Self-Organizing Maps to Learn Geometric Hash Functions for Model-Based Object Recognition. IEEE Transactions on Neural Networks. 9.3:560–570.
Bebis, G, Papadourakis GM, Georgiopoulos M.  1989.  Back-Propagation: Increasing rate of convergence by predictable pattern loading. 1:14-30.
Bebis, G, Georgiopoulos M, da Lobo VN, Shah M.  1999.  Learning affine transformations. Pattern Recognition. 32.10:1783–1799.
Bebis, G, Papadourakis GM, Georgiopoulos M, Heileman GL.  1992.  Increasing classification accuracy using multiple-neural-network schemes. Proceedings of the 1992 SPIE Conference (Technical Program on Intelligent Information Systems).
Bebis, G, Georgiopoulos M, da Lobo VN.  1998.  Using self-organizing maps to learn geometric hash functions for model-based object recognition. IEEE Transactions on Neural Networks. 9:560-570.
Bebis, G, Georgiopoulos M.  1995.  Improving generalization by using genetic algorithms to determine the neural network size. Southcon 95.
Bebis, G, Georgiopoulos M, Shah M, da Lobo VN.  1998.  Algebraic functions of views for indexing-based object recognition. Proceedings of the Sixth International Conference.
Bebis, G, Deaconu T, Georgiopoulos M.  1999.  Fingerprint identification using Delaunay triangulation. IEEE International Conference on Information,Intelligence and Systems. :452–459.
Bebis, G, Georgiopoulos M, Bhatia S.  1997.  Learning orthographic transformations for object recognition. 1997 IEEE International Conference on Systems, Man, and Cybernetics.
Bassiouni, M, Georgiopoulos M, Thompson J.  1989.  Real time simulation networking Network modeling and protocol alternatives. 11th Interservice / Industry Training Systems Conference.
Bassiouni, M, Georgiopoulos M, Chiu M.  1997.  Performance of standard and modified network protocols in a real-time application. 1997 International Performance, Computing and Communications Conference.
Bassiouni, M, Georgiopoulos M, Thompson J.  1990.  Analytical and simulation models for real time networks. Proceedings of the 21st Annual Pittsburgh Conference.
Bahr, H, Georgiopoulos M, DeMara R.  1997.  Integer-encoded massively parallel processing of fast learning Fuzzy ARTMAP neural networks. Proceedings SPIE; Conference of Applications and Science of Artificial Neural Networks III.