Abstract
This chapter reports about the application of pattern recognition methods from the area of “neural computation” exploring their capabilities for finding structure in a data base of conflict management events since 1945 (Confman: Bercovitch and Langley, 1993; see Chapter 6). In particular, the following two methods were tested
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References
Bercovitch, J., and Houston, A., 1993, Influence of mediator characteristics and behavior on the success of mediation in international relations, Journal of Conflict Resolution 37:297–321.
Bercovitch, J., and Langley, J., 1993, The nature of dispute and the effectiveness of international mediation, Journal of Conflict Resolution 37:670–691.
Bercovitch, J., Anagnoson, J., and Wille, D. L., 1991, Some conceptual issues and empirical trends in the study of successful mediation in international relations, Journal of Peace Research, 28:7–17.
Bishop, C., 1995, Neural Networks for Pattern Recognition, Clarendon Press, Oxford.
Dorffner, G., 2000, Neural networks in peace and conflict research: An overview of and proposals for possible uses, Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2000–03.
Duda, R., Hart, P., and Stork, D., 2000, Pattern Classification, 2nd ed., Wiley, New York.
Fürnkranz, J., Petrak J., and Trappl, R., 1997, Knowledge discovery in international conflict databases, Applied Artificial Intelligence 11:91–118.
Garson, G. D., 1998, Neural Networks: An Introductory Guide for Social Scientists, Sage, London.
Holmes, G., Donkin, A., and Witten, I. H., 1994, Weka: A machine learning workbench, In Proceedings of the Second Australia and New Zealand Conference on Intelligent Information Systems.
Kohonen, T., 1995, Selforganizing Maps, Springer, Berlin/New York.
Nabney, I., 2001, NETLAB: Algorithms for Pattern Recognition, Springer, New York.
Quinlan, R., 1993, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA.
Schrodt, P. A., 1991, Prediction of interstate conflict outcomes using a neural network, Social Science Computer Review 9:359–380.
Schrodt, P. A., 2000, Pattern recognition of international crises using Hidden Markov Models, in Political Complexity: Nonlinear Models of Politics, Richards, D., ed., University of Michigan Press, Ann Arbor, pp. 296–328.
Ultsch, A., 1991, Connectionist Models and their Integration with Knowledge Based Systems, Habilitationsschrift, Univ. Dortmund.
Vesanto, J., Himberg, J., Alhoniemi, E., and Parhankangas, J., 1999, Self-organizing map in Matlab: the SOM Toolbox, in Proceedings of the Matlab DSP Conference 1999, Espoo, Finland, November 16–17, pp. 35–40.
Wickboldt, A.-K., Bercovitch, J., 1996, Dynamics of international mediation: analysis using machine learning methods, Conflict Management and Peace Science 17(1).
Witten, I. H., and Frank, E., 1999, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, San Mateo, CA.
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Dorffner, G., Rattenberger, J., Hörtnagl, E., Bercovitch, J., Trappl, R. (2006). Neural Computation for International Conflict Management. In: Trappl, R. (eds) Programming for Peace. Advances in Group Decision and Negotiation, vol 2. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4390-2_9
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DOI: https://doi.org/10.1007/1-4020-4390-2_9
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