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Virtual organizations (VO) are geographically distributed groups of people sharing common goals and willingness to collaborate. One of the important roles of virtual organizations is to facilitate sharing resources related to the area of collaboration. This paper presents an approach to handling resources in Computational Intelligence and Machine Learning distributed over a large number of sites. Resources are first discovered in the internet, evaluated, and then shared among users. Reasoning and adaptation methods can then be applied to best fit resources into users’ needs without a long search process.
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Czasopismo
Rocznik
Tom
Strony
5--19
Opis fizyczny
Bibliogr. 12 poz., rys.
Twórcy
autor
- George Mason University 4400 University Drive, MSN 1J3, Fairfax, VA 22030, USA
autor
- University of Louisville, Louisville, KY 40292, USA
autor
- George Mason University 4400 University Drive, MSN 1J3, Fairfax, VA 22030, USA
autor
- University of Louisville, Louisville, KY 40292, USA
Bibliografia
- 1. Atanassov, K., Hadjiiski, M., 2008: Generalized nets as tools for modeling of intelligent systems. Proc. 4th Int. IEEE Conf. on Intelligent Systems, IIS ’08, Varna, Bulgaria.
- 2. Brazdil, P.B. and Soares C., 2000: Ranking Classification Algorithms Based on Relevant Performance Information. Proc. 11th European Conf. on Machine Learning, Workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination, Barcelona, Spain.
- 3. Carbonell, T.J., Michalski, R.S. and Mitchell, T.M., 1983: An Overview of Machine Learning. In Machine Learning: An Artificial Intelligence Approach, R.S. Michalski, T.J. Carbonell and T.M. Mitchell (Eds.), TIOGA Publishing Co., Palo Alto, pp. 3-23.
- 4. Coyle, M. and Smyth B., October 2007: Supporting intelligentWeb search. ACM Trans. on Internet Technology, 7, 4.
- 5. Gagliolo, M. and Schmidhuber, J, 2006: Dynamic algorithm portfolios. Annals of Mathematics and Artificial Intelligence.
- 6. Glowinski, C. and Michalski, R.S., 2001 June: Discovering Multi-head Attributional Rules in Large Databases. 10th Int. Symposium on Intelligent Information Systems, Zakopane, Poland.
- 7. Liang, J.J., Runarsson, T.P., Mezura-Montes, E., Clerc, M., Suganthan, P.N., Coello Coello, C.A., and Deb, K., 2005: Problem Definitions and Evaluation Criteria. CEC 2006 Special Session on Constrained Real-Parameter Optimization, Tech Rep., Nanyang Technical University, Singapore.
- 8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H., 2009: The WEKA Data Mining Software: An Update. SIGKDD Explorations, Volume 11, Issue 1.
- 9. Michalski, R.S., 1986: Dynamic Recognition: An Outline of Theory of How to Recognize Concepts without Matching Rules. Rep. of the Intelligent Systems Group, ISG 86-16, UIUCDCS-F-86-965, Urbana.
- 10. Vilalta, R. and Drissi, Y., 2002: A perspective view and survey of meta-learning. Artificial Intelligence Review, 18, 2, pp. 77-95.
- 11. Wojtusiak, J., Michalski, R. S., Kaufman, K. and Pietrzykowski, J., November 13-15, 2006: The AQ21 Natural Induction Program for Pattern Discovery: Initial Version and its Novel Features. Proc. of The 18th IEEE Int. Conf. on Tools with Artificial Intelligence, Washington D.C.
- 12. Zurada, J.M., Mazurowski, M.A., Abdullin, A., Ragade, R.,Wojtusiak, J. and Gentle, J.E., 2009: Building Virtual Community in Computational Intelligence and Machine Learning, Computational Intelligence Magazine, 4, 1, pp. 43-54.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-77e7e5d9-3b6b-4cff-9c08-7f9c1b027e10