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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, 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., da Lobo, V. N., and Shah, M., “Learning affine transformations”, Pattern Recognition, vol. 32.10, pp. 1783–1799, 1999.
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M. Zhong, Georgiopoulos, M., and Anagnostopoulos, G. C., “k-Norm Misclassification Rate Estimation for Decision Trees”, Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Soft Computing (ASC 2007), pp. 163–168, 2007.
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.
M. Zhong, Georgiopoulos, M., and Anagnostopoulos, G. C., “A k -norm pruning algorithm for decision tree classifiers based on error rate estimation”, Machine Learning, vol. 71, pp. 55-88, 2008.
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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.
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, 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.
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.
M. Dagley-Falls, Georgiopoulos, M., and Young, C., “Influencing sense of community in a STEM living-learning community: An NSF STEP funded project”, in Proceedings of the 2010 ASEE Conference and Exposition, 2010.
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, 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.
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.
M. J. Johnson, Georgiopoulos, M., Mollaghasemi, M., and McGinnis, M., “Improving human behavior in simulations – OneSAF tested baseline intelligent computer generated objects”, in SCS, Military, Government and Aerospace Simulation Symposium 2001 (MGA 2001) Conference, Seattle, Washington, 2001.
G. Bebis and Georgiopoulos, M., “Improving generalization by using genetic algorithms to determine the neural network size”, in Southcon 95, 1995.
C. Puklavage, Pirela, A., Gonzalez, A. J., and Georgiopoulos, M., “Imitating personalized expressions through an Avatar using Machine Learning”, in Proceedings of the 23rd International FLAIRS conference, 2010.
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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 and Georgiopoulos, M., “Hypershere ART and ARTMAP for unsupervised and supervised incremental learning”, in the International Joint Conference on Neural Networks, Como, Italy, 2000.
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.
S. Verzi, Heileman, G., Georgiopoulos, M., and Healy, M., “Hierarchical ARTMAP”, Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 41–46, 2008.
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.
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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-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.
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.
R. Miguez, Georgiopoulos, M., and Kaylani, A., “A Genetically Engineered Probabilistic Neural Network”, Nonlinear Analysis: Theory, Methods & Applications, vol. 73, pp. 1783-1791, 2010.
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.
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.
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. Reeder and Georgiopoulos, M., “Generative Neural Networks for Multi-Task Life-Long Learning”, The Computer Journal, vol. 57, no. 3, pp. 427-450, 2014.
M. Zhong, Georgiopoulos, M., Anagnostopoulos, G. C., and Mollaghasemi, M., “Generalized Entropy for Splitting Numerical Attributes in Decision Tree Classifiers”, Proceedings of the 19th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (FLAIRS 2006), pp. 604–609, 2006.
M. Georgiopoulos, Dagher, I., Heileman, G. L., and Bebis, G., “The generalization capabilities of ARTMAP”, in International Conference on Neural Networks, Houston, TX, 1997.
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, 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.
M. Zhong, Coggeshall, D., Georgiopoulos, M., Anagnostopoulos, G. C., Mollaghasemi, M., and Richie, S., “Gap-Based Estimation: Choosing the Smoothing Parameters for Probabilistic and General Regression Neural Networks”, Proceedings of the 2006 IEEE-INNS-ENNS International Joint Conference on Neural Networks, pp. 1870–1877, 2006.
M. Zhong, Coggeshall, D., Ghaneie, E. G., Pope, T., Rivera, M. A., Georgiopoulos, M., and Anagnostopoulos, G. C., “Gap-Based Estimation: Choosing the smoothing parameters for Probabilistic and General Regression Neural Networks”, in WCCI 2006, Vancouver, Canada, 2006.
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C. Sentelle, Hong, S. L., Georgiopoulos, M., and Anagnostopoulos, G. C., “A Fuzzy Gap-statistic for Fuzzy C-Means”, in The 11th IASTED International Conference on Artificial Intelligence and Soft Computing (ASC 2007), Palma de Malorca, Spain, 2007.
C. Sentelle, Hong, S. L., Anagnostopoulos, G. C., and Georgiopoulos, M., “A fuzzy gap statistic for Fuzzy C-Means”, Proceedings of the 11th IASTED International Conference on Artificial, 2007.
I. Dagher, Georgiopoulos, M., Heileman, G., and Bebis, G., “Fuzzy ARTVar: An Improved Fuzzy ARTMAP Algorithm”, International Joint Conference on Neural Networks, pp. 1688–1693, 1998.
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.
J. Huang, Georgiopoulos, M., and Heileman, G. L., “Fuzzy ART properties”, Neural Networks, vol. 8, pp. 203-213, 1995.
D. Charalampidis, Anagnostopoulos, G. C., Georgiopoulos, M., and Kasparis, T., “Fuzzy ART and ARTMAP with adaptive weighted distances”, Proceedings of SPIE,Conference on Applications and Science of Computational Intelligence V, vol. 4739, pp. 86–97, 2002.
G. Bebis, Deaconu, T., and Georgiopoulos, M., “Fingerprint identification using Delaunay triangulation”, IEEE International Conference on Information,Intelligence and Systems, pp. 452–459, 1999.
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.
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.
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.
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 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. 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.
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.
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C. Sentelle, Georgiopoulos, M., Anagnostopoulos, G. C., and Young, C., “On extending the SMO algorithm sub-problem”, Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2007.
M. Zhong, Rosander, B., Georgiopoulos, M., Anagnostopoulos, G., Mollaghasemi, M., and Richie, S., “Experiments with Safe Micro-ARTMAP: Effect of the Network Parameters on the Network Performance”, in WCCI 2006, Vancouver, Canada, 2006.
M. Zhong, Rosander, B., Georgiopoulos, M., Anagnostopoulos, G. C., Mollaghasemi, M., and Richie, S., “Experiments with Safe micro-ARTMAP: Effect of the Network Parameters on the Network Performance”, Neural Networks, vol. 20.2, pp. 245–259, 2007.
M. Zhong, Rosander, B., Georgiopoulos, M., Anagnostopoulos, G. C., Mollaghasemi, M., and Richie, S., “Experiments with Safe ARTMAP and Comparisons to Other ART Networks”, Proceedings of the 2006 IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2006), pp. 720–727, 2006.
M. Zhong, Rosander, B., Georgiopoulos, M., Anagnostopoulos, G., Mollaghasemi, M., and Richie, S., “Experiments with Micro-ARTMAP: Effect of the Network Parameters on the Network Performance”, in 2005 Artificial Neural Networks in Engineerin, St. Louis, MI, 2005.
M. Zhong, Georgiopoulos, M., and Anagnostopoulos, G. C., “Experiments with an Innovative Tree Pruning Algorithm”, in IASTED International Conference on Artificial Intelligence and Applications, Innsbruck, Innsbruck, 2007.
M. Georgiopoulos, Anagnostopoulos, G. C., and Bharadwaj, M., “Experimental comparisons of semi-supervised and supervised ART classifiers”, in The 16th International Flairs Conference, St. Augustine, FL, 2003.
L. Quang, Anagnostopoulos, G., Georgiopoulos, M., and Ports, K., “An experimental comparison of semi-supervised ARTMAP architectures, GCS, and GNC Classifiers”, in 2005 International Joint Conference on Neural Networks, Montreal, Quebec, 2005.
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.
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, “On the error probability of coded frequency hopped spread spectrum systems with codeword interleaving”, in MILCOM 91, McLean, Virginia, 1991.
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.
T. J. Frederick, Belkerdid, M., and Georgiopoulos, M., “Error control coding for meteor burst channels”, in MILCOM 91, McLean, Virginia, 1991.
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.
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.
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/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., “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 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., “Ellipsoid ART and ARTMAP for incremental clustering and classification”, Proceedings of the International Joint Conference on Neural Networks, pp. 1221–1226, 2001.
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.
C. Sentelle, Anagnostopoulos, G. C., and Georgiopoulos, M., “An efficient active set method for SVM training without singular inner problems”, in the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2009), Atlanta, GA, 2009.
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.
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G. L. Heileman, Georgiopoulos, M., and Abdallah, C., “A dynamical adaptive resonance architecture”, IEEE Transactions on Neural Networks, vol. 5, pp. 873-889, 1994.
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.
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.
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, Georgiopoulos, M., and Anagnostopoulos, G. C., “Detecting Outliers in High-Dimensional Datasets with Mixed Attributes”, International Conference on Data Mining (DMIN 2008), 2008.
C. Christodoulou, Huang, J., and Georgiopoulos, M., “Design of gratings and frequency-selective surfaces using ARTMAP neural networks”, Journal of Electromagnetic Waves and Applications, vol. 9, pp. 17-36, 1995.
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.
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. 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., 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., “A data partitioning approach to speed the Fuzzy ARTMAP algorithm using the Hilbert space-filling curve”, pp. 2367–2372, 2004.
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M. Georgiopoulos, Koufakou, A., Anagnostopoulos, G. C., and Kasparis, T., “Cross-Validation in Fuzzy ARTMAP Neural Networks for Large Sample Classification Problems”, Proceedings of SPIE; Applications and Science of Computational Intelligence IV, 2001.
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.
M. Georgiopoulos, Castro, J., Wu, A., DeMara, R., Gelenbe, E., and Gonzalez, A., “CRCD in machine learning at the University of Central Florida preliminary experiences”, Proceedings of the 8th annual conference on Innovation and Technology in Computer Science Education, p. 249, 2003.
M. Georgiopoulos, Castro, J., Gelenbe, E., DeMara, R., Gonzalez, A., and Kysilka, M., “CRCD Experiences at the University of Central Florida: An NSF Project”, Proceedings of the ASEE 2004 Annual Conference and Exposition, 2004.
M. Georgiopoulos, Gelenbe, E., DeMara, R., Gonzalez, A., Kysilka, M., Mollaghasemi, M., Wu, A., Russell, I., Anagnostopoulos, G., and Secretan, J., “CRCD Experiences at the University of Central Florida: An NSF Project”, in the ASEE 2005 Annual Conference and Exposition, Portland Oregon, 2005.
M. Georgiopoulos, Russell, I., Castro, J., Wu, A., Kysilka, M., and DeMara, R., “A CRCD Experience: Integrating machine learning modules into introductory engineering and science programming courses”, Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition, 2003.
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.
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., “Convergence properties of learning in ART1”, Neural Computation, vol. 2, pp. 502-510, 1990.
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. 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.
C. Li, Georgiopoulos, M., and Anagnostopoulos, G. C., “Conic Multi-Task Classification”, in ECML PKDD 2014, Nancy, France, September 15-19, 2014.
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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