A novel dynamic fusion method using localized generalization error model

@article{Yeung2009AND,
  title={A novel dynamic fusion method using localized generalization error model},
  author={Daniel S. Yeung and Patrick P. K. Chan},
  journal={2009 IEEE International Conference on Systems, Man and Cybernetics},
  year={2009},
  pages={623-628}
}
A new dynamic classifier fusion method named L-GEM Fusion Method (LFM) for Multiple Classifier Systems (MCSs) is proposed. The localized generalization error upper bound for the neighborhood of a testing sample is calculated and used to estimate the local competence of base classifiers in MCSs. Different from the recent dynamic classifier selection methods, the proposed method consider not only the training error but also the sensitivity of the base classifier. Experimental results show that… CONTINUE READING

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A Theoretical Analysis of Bagging as a Linear Combination of Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2008
View 1 Excerpt

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Communications and Discoveries from Multidisciplinary Data • 2008
View 1 Excerpt

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