Free Access
Issue
RAIRO-Oper. Res.
Volume 51, Number 2, April-June 2017
Page(s) 329 - 341
DOI https://doi.org/10.1051/ro/2016024
Published online 27 February 2017
  • S. Abe, Support Vector Machines for Pattern Classification. Advances in Pattern Recognition, 2nd edition. Springer, London, UK (2010).
  • K. Bennett and E. Bredensteiner, Geometry in Learning, in Geometry at Work, edited by C. Gorini. Mathematical Association of America, Washington D.C. (2000) 132–145.
  • K. Bennett and E. Bredensteiner, Duality and Geometry in SVMs, In Proc. of 17th International Conference on Machine Learning, edited by P. Langley. San Francisco (2000) 65–72.
  • N. Couellan, S. Jan, T. Jorquera and J.-P. Georgé, Self Adaptive Support Vector Machine: A Multi-Agent Optimization Perspective. Expert Syst. Appl. 42 (2015) 4284–4298. [CrossRef]
  • F. Facchinei and C. Kanzow, Generalized Nash Equilibrium Problems. Annals OR 175 (2010) 177–211. [CrossRef]
  • J. Fürnkranz, Machine Learning in Games: A Survey, in Machines that Learn to Play Games. Nova Science Publishers (2001) 11–59.
  • K. Hausken and R. Cressman, Formalization of Multi-level games. Int. Game Theory Rev. 6 (2004) 195–221. [CrossRef] [MathSciNet]
  • N. Japkowicz and S. Stephen, The class imbalance problem: A systematic study. Intel. Data Anal. 6 (2002) 429–449.
  • G. Koltsidas and F.-N. Pavlidou, A Game Theoretical Approach to Clustering of Ad-Hoc and Sensor Networks. Telecomm. Systems 47 (2011) 81–93. [CrossRef]
  • D.G. Luenberger, Optimization by Vector Space Methods, 1st edition. John Wiley & Sons, Inc., New York, USA (1997)
  • A. Neyman and S. Sorin (eds.), Stochastic Games and Applications, Proc. of the NATO Advanced Study Institute, Stony Brook, New York, USA, 1999, Series: Nato Science Series C: (closed), Vol. 570 (2003)
  • S. Parsons and M. Wooldridge, Game Theory and Decision Theory in Multi-Agent Systems. Autonomous Agents and Multi-Agent Systems 5 (2002) 243–254. [CrossRef]
  • M. Petrovskiy, A Game Theory Approach to Pairwise Classification with Support Vector Machines. Proc. of the 2004 International Conference on Machine Learning and Application. ICMLAs, IEEE Computer Society (2004) 115–122.
  • B. Schölkopf and A. Smola, Learning with Kernels. MIT, Cambridge (2002).
  • S. Sra, S. Nowozin and S.J. Wright, Optimization for Machine Learning. MIT Press, Cambridge (2011).
  • G.M. Weiss, Mining with Rarity: A Unifying Framework. ACM SIGKDD Explorations Newsletter 6 (2004) 7–19. [CrossRef]
  • G. Weiss, A modern Approach to Distributed Artifical Intelligence. Intelligent Robotics & Autonomous Agents Series, MIT Press, Cambridge (2000).
  • J. Weston and C. Watkins, Support Vector Machines for Multi-Class Pattern Recognition. Proc. of ESANN’1999, Bruges, Belgium (1999) 219–224.

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