Optimal combinations of pattern classifiers

@article{Lam1995OptimalCO,
  title={Optimal combinations of pattern classifiers},
  author={Louisa Lam and Ching Y. Suen},
  journal={Pattern Recognition Letters},
  year={1995},
  volume={16},
  pages={945-954}
}
To improve recognition results, decisions of multiple classifiers can be combined. We study the performance of combination methods that are variations of the majority vote. A Bayesian formulation and a weighted majority vote (with weights obtained through a genetic algorithm) are implemented, and the combined performances of 7 classifiers on a large set of handwritten numerals are analyzed. 
BETA

Figures, Tables, and Topics from this paper.

Explore Further: Topics Discussed in This Paper

Citations

Publications citing this paper.
SHOWING 1-10 OF 181 CITATIONS, ESTIMATED 22% COVERAGE

FILTER CITATIONS BY YEAR

1996
2018

CITATION STATISTICS

  • 18 Highly Influenced Citations

  • Averaged 4 Citations per year over the last 3 years

  • 14% Increase in citations per year in 2018 over 2017

References

Publications referenced by this paper.
SHOWING 1-10 OF 16 REFERENCES

Genetic Algorithms in Search, Optimization, and Machine Learning

  • D. E. Goldberg
  • Addison-Wesley,
  • 1989
Highly Influential
2 Excerpts

Results of the Second IPTP Character Recognition Competition and studies on multi-expert handwritten numeral recognition

  • T. Noumi, T. Matsui, I. Yamashita, T. Wakahara, T. Tsutsumida
  • Proc. 4th lnternat. Workshop on Frontiers in…
  • 1994
2 Excerpts

Similar Papers

Loading similar papers…