Kagan Tumer's Publications

• Sorted by Date • Classified by Publication Type • Classified by Research Category •

Classified by Publication Type

• Book • Journal Articles • Book Chapters • Refereed Conference Papers • Workshop/Symposium Papers • Other Conference Papers • Thesis •

Book

• Collectives and the Design of Complex Systems, K. Tumer and D. Wolpert, editors. Springer, New York, 2004.
  Available from the publisher's website.
  ISBN 0-387-40165-2
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Journal Articles

• Efficient Evaluation Functions for Evolving Coordination. A. K. Agogino and K. Tumer. Evolutionary Computation Journal, to appear, 2008.
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• Applications of Ensemble Methods (Guest Editorial). N. C. Oza and K. Tumer. Information Fusion, 9(1):2–3, 2008.
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• Classifier Ensembles: Select Real World Applications. N. C. Oza and K. Tumer. Information Fusion, 9(1):4–20, 2008.
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• Handling Communication Restrictions and Team Formation in Congestion Games. A. K. Agogino and K. Tumer. Journal of Autonomous Agents and Multi Agent Systems, 13(1):97–115, 2006.
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• Improving Search Algorithms by Using Intelligent Coordinates. D. H. Wolpert, K. Tumer, and E. Bandari. Physical Review E, 69, 2004.
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• Bayes Error Rate Estimation using Classifier Ensembles. K. Tumer and J. Ghosh. International Journal of Smart Engineering System Design, 5(2):95–110, 2003.
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• Input Decimated Ensembles. K. Tumer and N. C. Oza. Pattern Analysis and Applications, 6(1):65–77, 2003.
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• Robust Combining of Disparate Classifiers through Order Statistics. K. Tumer and J. Ghosh. Pattern Analysis and Applications, 5(2):189–200, 2002.
  Details     Download: [pdf] (324.6kB )  


• Collective Intelligence, Data Routing and Braess' Paradox. D. H. Wolpert and K. Tumer. Journal of Artificial Intelligence Research, 16:359–387, 2002.
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• Optimal Payoff Functions for Members of Collectives. D. H. Wolpert and K. Tumer. Advances in Complex Systems, 4(2/3):265–279, 2001.
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• Collective Intelligence for Optimization. D. H. Wolpert and K. Tumer. Pages 48-50 of collective article: ``Statistical Machine Learning for Large Scale Optimization'', J. Boyan, W. Buntine, and A. Jagota (Eds.), Neural Computing Surveys, 3:1–60, 2000.
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• Collective Intelligence for Control of Distributed Dynamical Systems. D. H. Wolpert, K. Wheeler, and K. Tumer. Europhysics Letters, 49(6), March 2000.
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• Ensembles of Radial Basis Function Networks for Spectroscopic Detection of Cervical Pre-Cancer. K. Tumer, N. Ramanujam, J. Ghosh, and R. Richards-Kortum. IEEE Transactions on Biomedical Engineeiring, 45(8):953–961, 1998.
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• Analysis of Decision Boundaries in Linearly Combined Neural Classifiers. K. Tumer and J. Ghosh. Pattern Recognition, 29(2):341–348, 1996.
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• Error Correlation and Error Reduction in Ensemble Classifiers. K. Tumer and J. Ghosh. Connection Science, 8(3/4):385–404, 1996.
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• Designing Genetic Algorithms for the State Assignment Problem. J. N. Amaral, K. Tumer, and J. Ghosh. IEEE Transactions on Systems, Man and Cybernetics, 25(4):687–695, April 1995.
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• Structural Adaptation and Generalization in Supervised Feedforward Networks. J. Ghosh and K. Tumer. Journal of Artificial Neural Networks, 1(4):431–458, 1994.
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Book Chapters

• Coordinating Multi-Rover Systems: Evaluation Functions for Dynamic and Noisy Environments. K. Tumer and A. K. Agogino. In S. Yang, editors, Evolutionary Computation in Dynamic and Uncertain Environments, pp. 371–388, Springer, 2007.
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• Efficient Reward Functions for Adaptive Multi-Rover Systems. K. Tumer and A. K. Agogino. In K. Tuyls, P. Jan't Hoen, S. Sen, and K. Verbeeck, editors, Learning and Adaptation in Multi Agent Systems, pp. 177–191, Lecture notes in AI, Springer, 2006.
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• Designing Agent Utilities for Coordinated, Scalable and Robust Multi-Agent Systems. K. Tumer. In P. Scerri, R. Mailler, and R. Vincent , editors, Challenges in the Coordination of Large Scale Multiagent Systems, pp. 173–188, Springer, 2005.
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• Providing Effective Access to Shared Resources: A COIN Approach. S. Airau, S. Sen, D. Wolpert, and K. Tumer. In G. Di Marzo Serugendo, A. Karageorgos, O. F. Rana, and F. Zambonelli, editors, Engineering Self-Organizing Systems: Nature-Inspired Approaches to Software Engineering, Lecture Notes in Computer Science, pp. 249–264, Springer-Verlag, 2004.
  Details     Download: [pdf] (208.5kB )  


• A Survey of Collectives. K. Tumer and D. Wolpert. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pp. 1–42, Springer, 2004.
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• Coordination in Large Collectives. K. Tumer and D. Wolpert. In Y. Bar-Yam, editors, Fifth International Conference on Complex Systems, Perseus Books, 2004.
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• Optimal Payoff Functions for Members of Collectives. D. H. Wolpert and K. Tumer. In F. Schweitzer, editors, Modeling Complexity in Economic and Social Systems, pp. 355–369, World Scientific, 2002.
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• Robust Order Statistics Based Ensembles for Distributed Data Mining. K. Tumer and J. Ghosh. In H. Kargupta and P. Chan, editors, Advances in Distributed and Parallel Knowledge Discovery, pp. 185–210, AAAI/MIT Press, 2000.
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• Linear and Order Statistics Combiners for Pattern Classification. K. Tumer and J. Ghosh. In A. J. C. Sharkey, editors, Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems, pp. 127–162, Springer-Verlag, London, 1999.
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• Integration of Neural Classifiers for Passive Sonar Signals. J. Ghosh, K. Tumer, S. Beck, and L. Deuser. In C.T. Leondes, editors, Control and Dynamic Systems---Advances in Theory and Applications, pp. 301–338, Academic Press, 1996.
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Refereed Conference Papers

• Regulating Air Traffic Flow with Coupled Agents. A. Agogino and K. Tumer. In Proceedings of the Seventh International Joint Conference on Autonomous Agents and Multi-Agent Systems, Estoril, Portugal, May 2008. To appear
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• Aligning social welfare and agent preferences to alleviate traffic congestion. K. Tumer, Z. Welch, and A. Agogino. In Proceedings of the Seventh International Joint Conference on Autonomous Agents and Multi-Agent Systems, Estoril, Portugal, May 2008. To appear
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• Evolving Distributed Agents for Managing Air Traffic. A. Agogino and K. Tumer. In Proceedings of the Genetic and Evolutionary Computation Conference, London, UK, July 2007.
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• Distributed Agent-Based Air Traffic Flow Management. K. Tumer and A. Agogino. In Proceedings of the Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems, pp. 330–337, Honolulu,HI, May 2007. Best paper award (out of 532 submissions)
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• QUICR-Learning for Multi-Agent Coordination. A. Agogino and K. Tumer. In Proceedings of the 21st National Conference on Artificial Intelligence, Boston, MA, July 2006.
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• Distributed Evaluation Functions for Fault Tolerant Multi Rover Systems. A. Agogino and K. Tumer. In Proceedings of the Genetic and Evolutionary Computation Conference, Seattle, WA, July 2006.
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• Efficient Agent-Based Cluster Ensembles. A. Agogino and K. Tumer. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multi-Agent Systems, Hakodate, Japan, May 2006.
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• Efficient Agent-Based Models for Non-Genomic Evolution. N. Gupta, A. Agogino, and K. Tumer. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multi-Agent Systems, Hakodate, Japan, May 2006.
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• Coordinating Simple and Unreliable Agents. K. Tumer. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multi-Agent Systems, Hakodate, Japan, May 2006. (short paper)
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• Multi Agent Reward Analysis for Learning in Noisy Domains. A. Agogino and K. Tumer. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems, Utrecht, Netherlands, July 2005.
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• Efficient Credit Assignment through Evaluation Function Decomposition. A. Agogino, K. Tumer, and R. Miikulainen. In The Genetic and Evolutionary Computation Conference, Washington, DC, June 2005.
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• Coordinating Multi-Rover Systems: Evaluation Functions for Dynamic and Noisy Environments. K. Tumer and A. Agogino. In The Genetic and Evolutionary Computation Conference, Washington, DC, June 2005.
  Details     Download: [pdf] (291.4kB )  


• Unifying Temporal and Structural Credit Assignment Problems. A. Agogino and K. Tumer. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multi-Agent Systems, New York, NY, July 2004.
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• Efficient Evaluation Functions for Multi-Rover Systems. A. Agogino and K. Tumer. In The Genetic and Evolutionary Computation Conference, pp. 1–12, Seatle, WA, June 2004.
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• Overcoming Communication Restrictions in Collectives. K. Tumer and A. Agogino. In Proceedings of the International Joint Conference on Neural Networks, Budapest, Hungary, July 2004.
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• Time-Extended Policies in Multi-Agent Reinforcement Learning. K. Tumer and A. Agogino. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multi-Agent Systems, pp. 1336–1337, New York, NY, July 2004.
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• Team Formation and Communication Restrictions in Collectives. A. Agogino and K. Tumer. In Proceedings of the Second International Joint Conference on Autonomous Agents and Multi-Agent Systems, Melbourne, Australia, July 2003. (short paper)
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• Classification of Aircraft Maneuvers for Fault Detection. N. C. Oza, I. Y. Tumer, K. Tumer, and E. Huff. In Proceedings of the Fourth International Workshop on Multiple Classifier Systems, pp. 375–384, Springer, Surrey, UK, June 2003.
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• Collectives for Multiple Resource Job Scheduling Across Heterogeneous Servers. K. Tumer and J. Lawson. In Proceedings of the Second International Joint Conference on Autonomous Agents and Multi-Agent Systems, Melbourne, Australia, July 2003. (short paper)
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• Learning Sequences of Actions in Collectives of Autonomous Agents. K. Tumer, A. Agogino, and D. Wolpert. In Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems, pp. 378–385, Bologna, Italy, July 2002.
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• Input Decimated Ensembles: Decorrelation through Dimensionality Reduction. N. C. Oza and K. Tumer. In Kittler, J. and Roli, F., editors, Proceedings of the Second International Workshop on Multiple Classifier Systems, pp. 238–249, Springer, Cambridge, UK, June 2001.
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• Optimal Reward Functions in Distributed Reinforcement Learning. D. H. Wolpert and K. Tumer. In Proceedings of the Second Asia-Pacific Conference on Intelligent Agent Technology (IAT-2001), Maebashi City, Japan, October 2001.
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• Reinforcement Learning in Distributed Domains: Beyond Team Games. D. H. Wolpert, J. Sill, and K. Tumer. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, pp. 819–824, Seattle, WA, 2001.
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• Collective Intelligence and Braess' Paradox. K. Tumer and D. H. Wolpert. In Proceedings of the Seventeenth National Conference on Artificial Intelligence, pp. 104–109, Austin, TX, 2000.
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• Adaptivity in Agent-Based Routing for Data Networks. D. H. Wolpert, S. Kirshner, C. J. Merz, and K. Tumer. In Proceedings of the fourth International Conference of Autonomous Agents, pp. 396–403, Barcelona, Spain, 2000.
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• Decimated Input Ensembles for Improved Generalization. K. Tumer and N. C. Oza. In Proceedings of the International Joint Conference on Neural Networks, Washington, D.C., July 1999.
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• General Principles of Learning-Based Multi-Agent Systems. D. H. Wolpert, K. Wheeler, and K. Tumer. In Proceedings of the Third International Conference of Autonomous Agents, pp. 77–83, May 1999.
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• Using Collective Intelligence to Route Internet Traffic. D. H. Wolpert, K. Tumer, and J. Frank. In Advances in Neural Information Processing Systems - 11, pp. 952–958, MIT Press, 1999.
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• A Mutual Information Based Ensemble Method to Estimate the Bayes Error. K. Tumer, K. D. Bollacker, and J. Ghosh. In Intelligent Engineering Systems through Artificial Neural Networks, pp. 17–22, ASME Press, 1998.
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• Classifier Combining through Trimmed Means and Order Statistics. K. Tumer and J. Ghosh. In Proceedings of the International Joint Conference on Neural Networks, pp. 757–762, Anchorage, AL, June 1998.
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• Spectroscopic Detection of Cervical Pre-Cancer through Radial Basis Function Networks. K. Tumer, N. Ramanujam, R. Richards-Kortum, and J. Ghosh. In Advances in Neural Information Processing Systems - 9, pp. 981–987, M.I.T. Press, 1997.
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• Estimating the Bayes Error Rate Through Classifier Combining. K. Tumer and J. Ghosh. In Proceedings of the Thirteenth International Conference on Pattern Recognition, pp. IV:695–99, Vienna, Austria, August 1996.
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• Limits to Performance Gains in Combined Neural Classifiers. K. Tumer and J. Ghosh. In Intelligent Engineering Systems through Artificial Neural Networks, pp. 419–424, St. Louis, November 1995.
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• Order Statistics Combiners for Neural Classifiers. K. Tumer and J. Ghosh. In Proceedings of the World Congress on Neural Networks, pp. I:31–34, INNS Press, Washington D.C., July 1995.
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• Boundary Variance Reduction for Improved Classification through Hybrid Networks (Invited Paper). K. Tumer and J. Ghosh. In Applications and Science of Artificial Neural Networks, Proceedings of the SPIE (Vol. 2492), pp. 573–584, April 1995.
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• A Framework for Estimating Performance Improvements in Hybrid Pattern Classifiers. K. Tumer and J. Ghosh. In Proceedings of the World Congress on Neural Networks, pp. III:220–225, INNS Press, San Diego, June 1994.
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• Applying Genetic Algorithms to the State Assignment Problem: A case study. J. N. Amaral, J. Ghosh, and K. Tumer. In Adaptive and Learning Systems, Proceedings of the SPIE (Vol. 1706), pp. 2–13, Orlando, FL, April 1992.
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Workshop/Symposium Papers

• Effective Policies for Resource Limited Agents. M. Knudson and K. Tumer. In AAMAS-07 Workshop on Adaptive and Learning Agents, Honolulu, HI, May 2007.
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• Learning agents for distributed and robust spacecraft power management. S. Airiau, K. Tumer, and A. Agogino. In AAMAS-06 Workshop on Adaptation and Learning in Autonomous Agents and Multi Agent Systems, Hakodate, Japan, May 2006.
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• Agent Reward Shaping for Alleviating Traffic Congestion. K. Tumer and A. Agogino. In AAMAS-06 Workshop on Agents in Traffic and Transportation, Hakodate, Japan, May 2006.
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• Reinforcement Learning in Large Multi-agent Systems. A. Agogino and K. Tumer. In AAMAS-05 Workshop on Coordination of Large Scale Multi-Agent Systems, Utrecht, Netherlands, July 2005.
  Details     Download: [pdf] (186.4kB )  


• Efficient Reward Functions for Adaptive Multi-Rover Systems. K. Tumer and A. Agogino. In AAMAS-05 Workshop on Learning and Adaptation in Multi-Agent Systems, Utrecht, Netherlands, July 2005.
  Details     Download: [pdf] (304.8kB )  


• Coordination in large Multi-Agent Systems. K. Tumer. In AAMAS-04 Workshop on Challenges in the Coordination of Large Scale Multiagent Systems, New York, NY, July 2004.
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• Providing Effective Access to Shared Resources: A COIN Approach. S. Airau, S. Sen, D. Wolpert, and K. Tumer. In AAMAS-03 Workshop on Engineering Self-Organizing Systems, Melbourne, Australia, July 2003.
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• Beyond Mechanism Design. D. H. Wolpert and K. Tumer. In ICM Game Theory Conference, ``Heterogeneous and Social Games'' Special Session, Qingdao, China, August 2002.
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• Overview of Design of Collectives. D. H. Wolpert and K. Tumer. In Robosphere 2002: Self Sustaining Robotic Ecologies, pp. 11–16, Moffett Field, CA., November 2002.
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• Reinforcement Learning in Distributed Domains: An Inverse game Theoretic Approach. D. H. Wolpert and K. Tumer. In 2001 AAAI Spring Symposium on Game Theoretic and Decision Theoretic Agents, Stanford, CA, March 2001.
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• Ensemble Techniques for Data Mining in Heterogeneous Environments. J. Ghosh and K. Tumer. In Institute for Operations Research and the Management Sciences Conferences, San Antonio, TX, November 2000.
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• Designing Reward Functions for Multi-Agent Systems: A Collective Intelligence Approach. K. Tumer and D. H. Wolpert. In ICML-2000 Workshop on Multi-Agent Learning: Theory and Practice, Stanford, CA, July 2000.
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• Illustration of the COIN Approach to the Design of Multi-Agent Systems. D. H. Wolpert and K. Tumer. In AGENTS-00/ECML-00 Workshop on Learning Agents, Barcelona, Spain, June 2000.
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• Strategic Behavior, Learning and the Efficient Allocation of Network Resources. A. M. Bell, W. Sethares, D. Lucking-Reiley, D. H. Wolpert, K. Tumer, and J. Frank. In International Congress on Networks, Groups and Coalitions, Manresa, Spain, May 1999.
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• Collective Intelligence. D. H. Wolpert and K. Tumer. In Fourth Workshop on Economics with Heterogeneous Interacting Agents, Genoa, Italy, June 1999.
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• Collective Intelligence for Optimization. D. H. Wolpert and K. Tumer. In IJCAI-99 Workshop on Statistical Machine Learning for Large Scale Optimization, Stockholm, Sweden, July 1999.
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• Collective Intelligence for Network Routing. D. Wolpert, K. Tumer, and A. M. Bell. In AAAI-99 Workshop, Orlando, FL, July 1999.
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• Classifier Combining: Analytical Results and Implications. K. Tumer and J. Ghosh. In Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms, workshop at the Thirteenth National Conference on Artificial Intelligence, Portland, OR, August 1996.
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Other Conference Papers

• Entropy Based Anomaly Detection Applied to Space Shuttle Main Engines. A. Agogino and K. Tumer. In Proceedings of the IEEE Aerospace Conference, Big Sky, MO, March 2006.
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• Robust Coordination of a Large Set of Simple Rovers. K. Tumer and A. Agogino. In Proceedings of the IEEE Aerospace Conference, Big Sky, MO, March 2006.
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• Complexity Signatures for System Health Monitoring. K. Tumer and A. Agogino. In Proceedings of the IEEE Aerospace Conference, Big Sky, MO, March 2005.
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• A Distributed and Adaptive Health and Mission Management Architecture. K. Tumer, S. Uckun, and A. Agogino. In Integrated Systems Health Managemet Conference, Cincinati, OH, August 2005. extended abstract
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• Sequence Recognition by Input Anticipation. K. Tumer and J. Ghosh. In Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Austin, TX, June 1994.
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• Integration of Local and Global Neural Classifiers for Passive Sonar Signals. J. Ghosh, K. Tumer, S. Beck, and L. Deuser. In Proceedings of the International Simulation Technology Conference, pp. 539–545, Houston, TX, November 1992.
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• A New Algorithm for Automatic Shoulder Bed Correction. K. Tumer, D. Torres, and R. Chemali. In Transactions of the Society of Professional Well Log Analysts' 32nd Annual Logging Symposium, SPWLA, Midland, TX, June 1991.
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Thesis

• Linear and Order Statistics Combiners for Reliable Pattern Classification. K. Tumer. Ph.D. Thesis, The University of Texas, Austin, TX, 1996.
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• Structural Adaptation and Generalization in Neural Networks. K. Tumer. Master's Thesis, The University of Texas, Austin, TX,1992.
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