Combining Meta-Learning with Multi-objective Particle Swarm Algorithms for SVM Parameter Selection: An Experimental Analysis

@article{Miranda2012CombiningMW,
  title={Combining Meta-Learning with Multi-objective Particle Swarm Algorithms for SVM Parameter Selection: An Experimental Analysis},
  author={P{\'e}ricles B. C. de Miranda and Ricardo B. C. Prud{\^e}ncio and Andr{\'e} Carlos Ponce de Leon Ferreira de Carvalho and Carlos Soares},
  journal={2012 Brazilian Symposium on Neural Networks},
  year={2012},
  pages={1-6}
}
Support Vector Machines (SVMs) have become a well succeeded technique due to the good performance it achieves on different learning problems. However, the SVM performance depends on adjustments of its parameters' values. The automatic SVM parameter selection is treated by many authors as an optimization problem whose goal is to find a suitable configuration of parameters for a given learning problem. This work performs a comparative study of combining Meta-Learning (ML) and Multi-Objective… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 15 REFERENCES

Cross-searching strategy for multi-objective particle swarm optimization

  • 2007 IEEE Congress on Evolutionary Computation
  • 2007
VIEW 13 EXCERPTS
HIGHLY INFLUENTIAL

Multi-objective optimization of support vector machines, in Yaochu Jin (Ed.), Multi-objective Machine Learning

C. Igel, T. Suttorp
  • Studies in Computational Intelligence,
  • 2006
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

A Multiple Objective Particle Swarm Optimization Approach Using Crowding Distance and Roulette Wheel

R. Santana, M. Pontes, C. Bastos-Filho
  • In Proceedings of ISDA
  • 2009
VIEW 2 EXCERPTS
HIGHLY INFLUENTIAL

Analysis of results

VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Multi-objective Optimization and Meta-learning for SVM Parameter Selection

Péricles B.C. de Miranda, Ricardo B.C. Prudencio, Carlos Soares
  • International Joint Conference on Neural Networks,
  • 2012
VIEW 2 EXCERPTS